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Vol. 67 Number 10

OCTOBER 2020

ANSWERS 20 | Evaluating IoT wireless protocols for industrial applications 22 | Four best practices for industrial wireless LANs

20-27

WIRELESS COVER: Instruments can be native WirelessHART or have a wired-HART-to-wireless adapter. Courtesy: Emerson COVER INSET: This example map simulates wireless coverage and attenuation in a facility, showing where coverage is reliable and where it’s not. Courtesy: Siemens Industry

INSIGHTS 6 | International: COVID-19 accelerates the digitalization of enterprises 8 | Integrator Update: Prioritizing next year’s control system integration projects 10 | Technology Update: How to use human and artificial intelligence with digital twins NEWS

14 | Long-time Control Engineering editor, Edward J. Kompass, dies; Advanced motor and generator design, Daniel B. Jones, dies; Redesigned mask offers greater protection; Headlines online 18 | Think Again: Control Engineering subscribers at work

24 | Cost-effective explosion protection for industrial wireless networks 26 | RFID improves precision and efficiency in automobile production 28 | Exploring the benefits of AI and machine learning 30 | How automated anomaly detection can maximize production 32 | Engineers develop methods for AI bottlenecks with machine-learning algorithms 33 | HMI software advancement for IIoT optimization 35 | Automation system integration helps packaging verification

p.33

INSIDE MACHINES

M1 | New industrial mobile robot safety standard, R15.08 M3 | Five tips for specifying mobile robots M4 | How hollow-shaft encoders break the multi-turn barrier

CONTROL ENGINEERING (ISSN 0010-8049, Vol. 67, No. 10, GST #123397457) is published 12x per year, Monthly by CFE Media and Technology, LLC, 3010 Highland Parkway, Suite #325 Downers Grove, IL 60515. Jim Langhenry, Group Publisher/Co-Founder; Steve Rourke CEO/COO/Co-Founder. CONTROL ENGINEERING copyright 2020 by CFE Media and Technology, LLC. All rights reserved. CONTROL ENGINEERING is a registered trademark of CFE Media and Technology, LLC used under license. Periodicals postage paid at Downers Grove, IL 60515 and additional mailing offices. Circulation records are maintained at 3010 Highland Parkway, Suite #325 Downers Grove, IL 60515. Telephone: 630/571-4070. E-mail: ctle@omeda.com. Postmaster: send address changes to CONTROL ENGINEERING, PO Box 348, Lincolnshire, IL 60069. Publications Mail Agreement No. 40685520. Return undeliverable Canadian addresses to: PO Box 348, Lincolnshire, IL 60069. Email: ctle@omeda.com. Rates for nonqualified subscriptions, including all issues: USA, $165/yr; Canada/Mexico, $200/yr (includes 7% GST, GST#123397457); International air delivery $350/yr. Except for special issues where price changes are indicated, single copies are available for $30 US and $35 foreign. Please address all subscription mail to CONTROL ENGINEERING, PO Box 348, Lincolnshire, IL 60069. Printed in the USA. CFE Media and Technology, LLC does not assume and hereby disclaims any liability to any person for any loss or damage caused by errors or omissions in the material contained herein, regardless of whether such errors result from negligence, accident or any other cause whatsoever.

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October 2020

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input #4 at www.controleng.com/information

OCTOBER 2020

INNOVATIONS 41 | 2020 Engineers’ Choice Finalists 44 | New Products for Engineers: Extended protection safety light curtains, Temperature transmitter and signal converter, I/O system field, Module for monitoring efficiency values See more New Products for Engineers at www.controleng.com/NPE.

BACK TO BASICS

45 | Industrial Internet of Things vocabulary terms updated

NEWSLETTER: Industrial Networking • Shuttle system gets smart to meet e-commerce demands • Engineer’s perspective of the future of engineering applications • Bridging the artificial intelligence skills gap for machine manufacturers • PC control redefines intralogistics distribution center efficiency • Advanced capabilities are being incorporated into EAM systems Keep up with emerging trends: subscribe. www.controleng.com/newsletters.

CFE EDU: Virtual Training Week Attend CFE Media and Technology’s Virtual Training Week to receive training on a variety of the latest industry trends. Register and receive full access to exclusive content offered by industry experts with live Q&A sessions! Check out virtual training week at: www.controleng.com/virtual-training-week

Control Engineering digital edition The tablet and digital editions provide links to additional article images and text online and links to other related, useful resources. Learn more and register to download: www.controleng.com/magazine Control Engineering eBook series: SCADA & HMI eBook Fall Edition Supervisory control and data acquisition (SCADA) systems and humanmachine interfaces (HMIs) are a mainstay for engineers and allow them to better perform their tasks in a variety of industries. This helpful eBook from NKK Switches, Trihedral and Control Engineering features articles on topics such as automation and edge computing, embedded HMIs and augmented reality tools. Learn more and register to download: www.controleng.com/ebooks/. Oil & Gas Engineering helps maximize uptime and increase productivity through the use of industry best practices and new innovations, increase efficiency from the wellhead to the refinery by implementing automation and monitoring strategies, and maintain and improve safety for workers and the work environment. Read the digital edition at www.oilandgaseng.com.

controleng.com provides new, relevant automation, controls, and instrumentation content daily, access to databases for new products and system integrators, and online training.

www.controleng.com

control engineering

October 2020

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INSIGHTS INTERNATIONAL

Stone Shi, Control Engineering China

COVID-19 accelerates the digitalization of enterprises Facing pressure and uncertainty from COVID-19, manufacturing companies need to improve innovation, agility and adaptability as it applies to products, services, operations and employees.

n 2020, COVID-19 is having a huge impact on global industrial companies in their markets, workforce structure and more investments in rapid digitalization to enable remote operations and flexible innovation. According to IDC estimates, a quarter of the world’s companies are in recession. Work patterns have changed in the past four months more than a few years ago. Siemens even announced it will form a new normal of working pattern in the future, with “mobile office” and “traditional office” complementing each other. With COVID-19, markets around the world have undergone great changes. More companies also have begun to rely on telecommuting and are striving to build an intelligent and flexible supply chain. Before COVID-19, many analysts predicted it would take two years or more for the industry to complete digital transformation. It turned out these tasks were realized within 10 weeks of 2020.

Innovation, agility, adaptability

Facing the severe pressure, challenges and uncertainties brought about by COVID-19, it is necessary for manufacturing companies to improve innovation, agility and adaptability KEYWORDS: Digital as it applies to products, services, operations transformation, COVID-19, and employee developments. manufacturers The only way to solve these problems is COVID-19 continues to accelerating the digital transformation of change how factories work. enterprises, and use digital tools and deep Manufacturing digital integration of business processes to make transformation is accelerating online working, conferencing, training and innovation, agility and adaptability. even remote factory operations the “new norFactory labor shortages can mal.” This allows operating losses caused by be helped with automation, COVID-19 to be hedged in the short-term 5G, smart factories. and long-term business management models CONSIDER THIS to be explored. Are your investments Employees in manufacturing companies keeping pace with your aren’t always in an office; they may be in a faccompetitors’ digital tory, on the shop floor or on the sales and sertransformation acceleration? vice floor. Digital transformation can succeed ONLINE if the use of digital technology is narrowed If reading from the digital down to the worker’s work environment. edition, click on the headline For those on the front lines of manufacturfor more resources. www. ing companies, new technologies, such as augcontroleng.com/magazine mented reality (AR), allow them to interact www.controleng.com/ international with realistic environments, more and more

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complex products and information in many information technology (IT) systems, helping them quickly obtain 3D through intelligence. With wearable devices, it is possible to view equipment and access expert guidance remotely without going to the factory floor.

Faster digital transformation

For manufacturing companies, labor shortages and rising labor costs have been one of the driving forces behind digital transformation in recent years. The higher level of intelligence in response to COVID-19 and the higher pace and efficiency of work in the factories are why companies must speed up the process of digital transformation. Some reports said, in the process of resuming work in some smart factories, it only took one night to prepare for the emergency resumption of production, to ensure the quality and quantity of equipment needed. Capacity utilization rate at the beginning of the resumption of work reached 80%. Compared with traditional factories, the number of workers in smart factories has dropped by at least half, but production efficiency can be increased by 2.5 to 3 times. This increase in online demand for COVID-19 prevention and control undoubtedly has boosted digital applications, creating many new digital scenarios and increasing the initiative for digital transformation of enterprises. In particular, some large state-owned enterprises have joined in, which will add to the overall impact on the digital transformation of enterprises and more deeply influence the digitalization process of the industrial supply chain. COVID-19 has promoted society’s reliance on and adaptation to digitalization and network economy, accelerated the process of social digital transformation. COVID-19 also stimulated the application of 5G technology and industrial Internet platforms. Greater industrial internet and 5G use allows enterprises to feel the efficiency improvement brought about by digital technology for the first time on a large scale, opening the prelude to the large-scale popularization of relevant applications. ce

Stone Shi is executive editor-in-chief, Control Engineering China. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. www.controleng.com

input #5 at www.controleng.com/information

INSIGHTS

INTEGRATOR UPDATE Rick Slaugenhaupt, Maverick Technologies

Prioritizing next year’s control system integration projects Get six system integrator insights for future industrial automation and control system projects.

ure, it’s still this year, but it’s time to look ahead to next year’s projects with insights from system integrators to plan and prioritize upcoming projects. A facility walkthrough with an outside expert may reveal opportunities you didn’t know about.

1. What insights can you offer to help prioritize

2021 control system integration or other automation integration projects? There’s an obvious difference between projects that mitigate end-of-life/obsolete hardware issues and those intended to improve operations. While both are important, avoiding unexpected and/or prolonged downtime due to failure of a hard-to-find obsolete component carries the most risk. Combining these life-extendKEYWORDS: System ing projects with ones that improve operating integration, control system projects, automation performance at the same time should always upgrades be considered as the best bang for the buck.

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Combine life-extending projects with those that improve operating performance. Simulation is most important for projects that will modify continually running operations and where nonproducing time is rarely available. Establish metrics, if not for project justification, then for later use.

2. What should be included in a return on

investment (ROI) calculation? The most important aspect to consider in ROI calculation is the benchmark metric upon which the calculation is based. It may be difficult to establish but using something that isn’t precise or not clearly linked to outcomes from the project in question can make the ROI vague or meaningless.

3. How can an organization discern if

CONSIDER THIS

the next step should be a pilot project or a full-scale implementation after proof of concept?

Have you prioritized your next three automation integration projects?

Six insights for industrial automation system integration projects 1. Avoid downtime and increase performance. 2. Take care with benchmark metrics. 3. Consider risk and opportunity to choose between a pilot or full-scale project. 4. Simulation helps implementation, optimization and training. 5. Observe: Don’t walk right by a needed automation upgrade. 6. Develop metrics for later use, if not for project justification. Courtesy: Maverick Technologies

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control engineering

Risk and opportunity are the biggest drivers for deciding if pilot projects are warranted or can be bypassed.

4. Do simulations/digital twins have a greater role? Simulation is most important for projects that will modify those operations that must run continually and where non-producing time is rarely available. (There’s a small, time window to deploy and make it work.) Using simulation beyond a one-time project implementation can be very beneficial for on-going performance tuning and training, but it requires dedicated effort to maintain the simulation model so it keeps pace with reality. This adds operating expense (OPEX) cost on top of the project’s capital expense (CAPEX) cost and makes financial justification more difficult but can pay long-term dividends – especially if it’s part of a larger effort to establish a digital twin.

5. What opportunities can a facility walk-through

with a system integrator reveal? One seldom-obvious problem that can only be revealed by a walkthrough is how much manual or redundant/repeated effort is required by operators in the normal course of operations. This behind-thescenes inefficiency can be a significant opportunity for automation upgrades, but is often not considered when evaluating the cost/benefit of potential projects even though it can be a big contributor to lessthan-ideal performance and unforeseen events due to poor operational awareness.

6. What other factors should be considered?

We know it’s often difficult to financially justify automation improvements due to the lack of clear benchmark metrics. An overlooked outcome to consider for every project should be the establishment of these type of metrics for later use. It will streamline the initial approval process for subsequent projects and produce truly representative ROI numbers afterward that will show the real, bottom-line value of undertaking automation improvements. ce

Rick Slaugenhaupt is consultant, Maverick Technologies, a CFE Media content partner; Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. www.controleng.com

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INSIGHTS

TECHNOLOGY UPDATE Michael Thomas, Brad Klenz and Prairie Rose Goodwin, SAS, Industrial Internet Consortium

How to use human and artificial intelligence with digital twins Industrial Internet of Things (IIoT), artificial intelligence (AI), user interface technologies such as augmented reality and virtual reality can help the form and function of digital twins to improve training, operations and outcomes.

uman intelligence has been creating and maintaining complex systems since the beginnings of civilizations. In modern times, digital twins have emerged to aid operations of complex systems, as well as improve design and production. Artificial intelligence (AI) and extended reality (XR) – including augmented reality (AR) and virtual reality (VR) – have emerged as tools that can help manage operations for complex systems. Digital twins can be enhanced with AI and emerging user interface (UI) technologies like XR can improve people’s abilities to manage complex KEYWORDS: Artificial intelligence, digital twins, systems via digital twins. virtual reality Digital twins can marry human and AI Digital twins get help from to produce something far greater by creataugmented reality and ing a usable representation of complex sysartificial intelligence. tems. End users do not need to worry about Data visualization tools the formulas that go into machine learning date back to 1700s, AR and VR are modern extensions. (ML), predictive modeling and artificially

M INSIGHTS

Figure 1: A digital twin can be enhanced with artificial intelligence (AI) and intelligent realities user interfaces, such as extended reality (XR), which includes augmented reality (AR) and virtual reality (VR). Courtesy: SAS and IIC

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intelligent systems, but also can capitalize on their power as an extension of their own knowledge and abilities. Digital twins combined with AR, VR and related technologies provide a framework to overlay intelligent decision making into day-to-day operations, as shown in Figure 1.

What’s needed to form and feed a digital twin?

The operations of a physical twin can be digitized by sensors, cameras and other such devices, but those digital streams are not the only sources of data that can feed the digital twin. In addition to streaming data, accumulated historical data can inform a digital twin. Relevant data could include data not generated from the asset itself, such as weather and business cycle data. Also, computer-aided design (CAD) drawings and other documentation can help the digital twin provide context. AI and other analytical models can take raw data and process it into forms that help humans understand the system. AI also can make intelligent choices of content on the user’s behalf. Such guidance could be very welcome to users because user input facilities are very different from the typical keyboard and mouse. As displayed in the upper right corner of Figure 1, humans can perceive the system as an intelligent reality – a technologically enhanced reality that can aid their cognition and judgement. With the blueprint in Figure 1 as a basis, it’s possible to create digital twins that use AI and reality technologies to achieve operational benefits. Any number of operations could be enhanced with the techniques described here. For example, the paper “Augmented Reality (AR) Predictive Maintenance System with Artificial Intelligence (AI) for Industrial Mobile Robot” details how a machine learning model can be used to classify the state of a robot motor which can then be presented to factory personnel with AR. This article applies the blueprint concepts to facilities management after first exploring each concept in depth. While the various data streams reach their conclusions in human perwww.controleng.com

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INSIGHTS

TECHNOLOGY UPDATE analytics and AI without requiring a new hardware paradigm, like an AR headset. AR headsets have the potential to benefit operations, but only if applications are successfully designed for usability, which is the next consideration. An outline follows of how to build a digital twin interface for remote experts.

Visualizing digital twin output across the UI spectrum

Figure 2: Using SAS Visual Analytics and Autodesk Forge software can enable system integration. In this example, Autodesk Forge is integrated into the reporting interface of SAS Visual Analytics.

ception, the starting point of a digital twin for a user is how it is perceived. Thus, the starting point for this exploration are user interfaces for digital twins, followed by a discusRead this online for 6 more images, sion of AI. a table “Five technological approaches

M ONLINE

for rendering digital twins” and more than 7 added pages of instruction, including sections on:

- Augmented reality usability, learnability, efficiency, learning advantages, low error rate, satisfaction, remote expert access - From IoT sensors to artificial intelligence - Common practices of creating artificial intelligence - Recurrent neural networks - How to train a recurrent neural network - Recurrent neural network forecasts - Reinforcement learning, machine learning, HVAC - Hyperparameter tuning for deep learning -Machine vision and digital twins - Create a digital twin with machine vision - Digital twin applications for smart facilities - Digital twin models: AI, analytical models - Always aware building management via digital twin, AR - Digital twin, IoT, AI, AR and other user interfaces.

If reading from the digital edition, click on the headline for more resources including a link to the full PDF IIC article,” Artificial and human intelligence with digital twins” with URLs for 18 references. www.controleng.com/magazine Or search on the headline at www.controleng.com.

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October 2020

Human reality of digital twins

Humans have a long history of interfacing with data and data visualization, starting with William Playfair’s inventions of line, bar and pie charts in the late 1700s. Digital twins can present data in such familiar forms, but the traditions of the late eighteenth century should not restrain the digital twin’s power. When using mobile technologies such as tablets, smart phones and AR headsets, the digital reality is overlaid on the physical reality into one view. AR headsets may be the obvious choice for this use case, but it is not the only one. Traditional interfaces rendering 3D models also allow workers to take advantage of digital twins. The first step in considering the creation of intelligent realities for digital twins is understanding data visualization options across the user interface (UI) spectrum. Next, a reporting integration approach is considered which can operationalize

control engineering

In Cap Gemini’s “Augmented and Virtual Reality in Operations” report, Jan Pflueger from Audi’s Center of Competence for AR/VR encouraged a business-first approach for reality projects. “First, focus on your use case and not on the technology itself. After you identify your use case, focus on your information handling and data so you can deliver the right information to the technology.” Consider five technological approaches for rendering digital twins and their respective capabilities. These are traditional desktop; smart phone or tablet; monocle AR; stereoscopic AR, including mixed reality (MR) devices; and immersive VR. With this article online, see a table for comparison. Within each class of device, capabilities vary, and the variance may affect a product’s viabilities for different use cases. This is especially true for AR headsets. Display resolution, field-of-view and computational power differ from product to product. In addition, design decisions about whether to put battery and compute units on the headset or on a separate tethered module can affect comfort and practicality. One practical concern for AR headsets is how they integrate with work clothing and uniforms such as those required for clean room and food processing operations.

Reporting with a digital twin context

Given an interactive visual analytics application, intelligent reality reports can be created with integrated 3D models like the one shown in Figure 2. The digital twin presents a custom visualization that can interact with other objects in the report, including showing data in a table or graph. This visualization approach adheres to long standing data presentation traditions without requiring new hardware beyond a regular desktop setup. The user interface is presented on a typical computer with a mouse and keyboard. Users need little additional training to use the power of the digital twin. ce

Michael Thomas is senior systems architect; Brad Klenz is distinguished systems architect; and Prairie Rose Goodwin is senior product developer with SAS Institute, an Industrial Internet Consortium (IIC) member. IIC is a Control Engineering content partner. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, mhoske@cfemedia.com. www.controleng.com

input #8 at www.controleng.com/information

INSIGHTS

NEWS

Long-time Control Engineering editor, Edward J. Kompass, dies A Control Engineering editor for 33 years, Edward J. Kompass passed away at age 93 on Aug. 31, 2020, in his South Lincoln, Vt., home. After retirement as editorial director, he was a regular contributor, then less-frequent contributor, finishing with a 50th Control Engineering anniversary column, “We never could have dreamed,” in 2004. Kompass helped write or edit more than 400 monthly issues, including when print editions were regularly more than 150 pages. While publishing didn’t yet include websites, webcasts or even email, during Kompass’ tenure typesetting moved from manual to automatic to electric, and printing presses were becoming more integrated with precision automation, controls and electronics to reduce waste, improve quality, and increase throughput. These aspects have been central themes of Control Engineering coverage; to help those working with automation do their jobs better. Control Engineering has been published by CFE Media and Technology since 2010. His daughter, Julie, one of six children, offered the following details from the Ed Kompass obituary: • Was born Dec. 22, 1926 in Jersey City, N.J. • Served in the U.S. Navy from 1944 to 1946 as an Electronic Technicians’ Mate Third Class on an LST (Landing Ship, Tank) helping return soldiers from the Pacific region at the end of WWII. •Earned a Bachelor’s degree in mechanical engineering from Stevens Institute of Technology, Hoboken, N.J., specializing in mechanical and electrical engineering. • Worked as a designer of computer peripheral equipment and servo control systems. • Started with McGraw-Hill, June 1, 1954, and worked on the first Control Engineering issue in September 1954. • Received the prestigious ABP (American Business Press) Crain Award in 1986, honoring those who made outstanding career contributions in editorial excellence.

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October 2020

• Had diverse interests including word games, puzzles, woodworking, model trains, hi-fi stereo, jazz music and just about anything he could take apart and repair. While fixing a radio, he reportedly designed and fabricated a capacitor from wires and paper.

ing’s maturation and worldwide recognition – including active promotion of the magazine through various national and international conferences and exhibitions including International Federation of Automatic Control (IFAC), the Purdue conference [Control Engineering’s Advanced Control Conference, a collaboration with Dr. Theodore J. Williams of Purdue University], and the Control Engineering shows [a series of annual automation conferences in venues including McCormick Place.]”

On computer control, 2004

A Control Engineering editor for 33 years, Edward J. Kompass passed away at age 93 on Aug. 31, 2020, in his South Lincoln, Vt., home. He’s pictured here in the publication’s Barrington, Ill., office in the early 1980s. Courtesy: Julie Wilhm, Kompass’ youngest daughter The Control Engineering cover in October 1965 said: “The magazine of Instrumentation, control, systems, design.” Coverage 55 years ago in October included ultra-cold process measurements, inertial sensors, twospeed servos and analog simulators. The magazine began in 1954. Courtesy: Control Engineering

Former Control Engineering executive editor, Frank Bartos (retired), was hired by Kompass in 1986 and worked on the publication for 20 years (and more after retirement). Bartos said: “What I remember most about Ed was his combined skills of editor, writer and engineer that extended into the theoretical and practical parts of the business. He also paved the way for Control Engineer-

control engineering

From the 2004 Control Engineering column, Kompass said, “We saw electronic digital computers coming as the means to match computing solution times to faster industrial control problems. So in 1955 Control Engineering editors started publication of two parallel series of articles on “Basic Digital Computer Theory” and “Digital Computer Applications in Control” that ran through 1956. But in 1955, IBM and Univac called their machines “electronic data processors,” not digital computers. IBM 700-series mainframes stored their data, and their programs for processing data, on punched cards. Electronic components were vacuum tubes. Compare mainframe-computing speeds quoted by IBM for the 700 series as “14,000 operations per second” with today’s claims for its fastest computers measured in terabytes per second. That’s a computing speed gain of a billion times in 50 years! We never would have guessed it would be that great.” Kompass is survived by his wife, Amelia (Emmy) of 69 years, six children, seven grandchildren, and four great grandchildren. ce Mark T. Hoske is content manager, Control Engineering, CFE Media, mhoske@cfemedia.com. Among Hoske’s first assignments after his 1994 start at Control Engineering was editing contributed columns from Ed Kompass, who he subsequently met at a Control Engineering Editors’ Choice Awards ceremony (precursor to the Engineers’ Choice Awards). www.controleng.com

Digital edition? Click on headlines for more details. See news daily at www.controleng.com

Advanced motor, generator designer, Daniel B. Jones, dies

aniel B. Jones, president, Incremotion Associates/Motion Media Group, of Thousand Oaks, Calif., died on Sept. 1, at 84 years of age, according to his wife, Jan Jones. Dan Jones was an electric motor and generator design engineer and worked in the motion control industry more than 60 years, including designs of high-torque, high-power density brushless permanent magnet and brush permanent magnet motors, and high-efficiency ac induction motors and generators. While Jones represented motor companies, often on twice a year press tours, he also was a motor designer, lecturer, and advanced-motor technology advocate. His motor designs ranged from 2 to 500 W, according to a 2015 Control Engineering story, that said Jones received the 2014 “Outstanding Contributions to the Electric Machines

Industry” lifetime achievement award from The Electronic Motor Education and Research Foundation (EMERF) in cooperation with the SMMA-Motors and Motion Association. The story said Jones had written and presented more than 265 articles and papers on motion control in the U.S., Europe and Asia. Burnet D. Brown, chief executive officer, GreenTech Motors Corp., called Jones “the Rosetta stone of electric motor technology.” Frank Bartos, former Control Engineering executive editor (retired) said Jones worked with Control Engineering on many articles (see a few links below): “With either Dan as the author or as the acknowledged invaluable contributor to in-house written articles. We were able to exchange ideas on electronic motor technology off and on – with Dan supplying the majority of ideas. He will be sorely missed.”

Mars mission motor design According to information from wife Jan and George Gulalo, long-time friend and president of MTT Technical Services, Dan Jones: -Designed a motor that went to Mars in a NASA exploratory mission. -Was a member of UL 1004 Standards Technical Panel since 2004, Advisory Board PCIM Exhibition and Conference member since 1985, MCA Board of Director’s Member since 2007, Member of AIME Board of Directors since 1994 and member of NEMA Technical Standards Review Committee for Servos and Step Motors since 1997. -Played softball into his early 70s. Jones is survived by his wife Janice of 60 years and his children Matt, Tom and Sue, 12 grandchildren, 7 great grandchildren and was preceded in death by his daughter Jennifer. ce See more with this article online.

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Digital edition? Click on headlines for more details. See news daily at www.controleng.com

NEWS

Redesigned mask offers greater protection, comfort Imagine a reusable face mask that protects wearers and those around them from COVID-19, is comfortable enough to wear all day, and stays in place without frequent adjustment. Based on decades of experience with filtration and textile materials,

Georgia Institute of Technology researchers designed a new mask intended to do just that — and are providing the plans so individuals and manufacturers can make it. The modular Georgia Tech mask combines a barrier filtration material with a

stretchable fabric to hold it in place. Prototypes made for testing use hook and eye fasteners on the back of the head to keep the masks on, and include a pocket for an optional filter to increase protection. After 20 washings, the prototypes have not shrunk or lost their shape. “If we want to reopen the economy and ask people to go back to work, we need a mask that is both comfortable and effective,” said Sundaresan Jayaraman, the Kolon Professor in Georgia Tech’s School of Materials Science and Engineering. The flaw in existing reusable cloth masks is they leak air around the edges. That potentially allows virus particles in aerosols, to enter the air breathed in by users, and allows particles from infected persons to exit the mask. COVID-19 increased the need for face masks because of the role played by asymptomatic and pre-symptomatic exposure from persons unaware they are infected, Jayaraman said. While the proportion of aerosol contributions to transmission is under study, aerosols likely increase the importance of formfitting masks. ce John Toon, Georgia Tech University. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

Headlines online Top 5 Control Engineering articles, Sept. 7-13, 2020 Featured articles include preparing for Industry 5.0, wastewater treatment teams searching for COVID-19, EtherCAT an IIoT platform built on battery-free sensors and edge I/O. Atomic dynamics: heat into electricity An atomic mechanism that makes some thermoelectric materials efficient near hightemperature phase transition could help unlock better options for technologies reliant on transforming heat into electricity. Data processing module makes deep neural networks smarter Combining feature normalization and feature attention modules into one module called attentive normalization (AN) improved deep neural networks’ performance.

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Being at work includes finding new technologies to examine, evaluate, and if appropriate, apply, integrate and use. Many subscribers buy or help specify technologies that they apply or recommend. In this issue, you’ll find a list of 88 Engineers’ Choice finalists in 21 categories. Please use your expertise to review the product descriptions and vote for the most useful. Think again about encouraging others, and thanks again. ce

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CFE Media and Technology Contributor Guidelines Overview Content For Engineers. That’s what CFE Media stands for, and what CFE Media is all about – engineers sharing with their peers. We welcome content submissions for all interested parties in engineering. We will use those materials online, on our website, in print and in newsletters to keep engineers informed about the products, solutions and industry trends. www.controleng.com/contribute explains how to submit press releases, products, images and graphics, bylined feature articles, case studies, white papers, and other media. * Content should focus on helping engineers solve problems. Articles that are commercial or are critical of other products or organizations will be rejected. (Technology discussions and comparative tables may be accepted if non-promotional and if contributor corroborates information with sources cited.) * If the content meets criteria noted in guidelines, expect to see it first on our Websites. Content for our e-newsletters comes from content already available on our Websites. All content for print also will be online. All content that appears in our print magazines will appear as space permits, and we will indicate in print if more content from that article is available online. * Deadlines for feature articles intended for the print magazines are at least two months in advance of the publication date. Again, it is best to discuss all feature articles with the appropriate content manager prior to submission. Learn more at: www.controleng.com/contribute

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People, Processes & Pandemics A n In terac ti ve W h i te Pa per Paul J. Galeski | founder of MAVERICK Technologies

When the new year rolled around and 2020 was finally here, it seemed the perfect time to look back and reflect on all the progress made in manufacturing technology since 2010. The rapid advance of technology will no doubt revolutionize our approach to manufacturing yet again in the coming decade. I had intended to share my vision of that future, to tackle the question, “What will manufacturing look like in the year 2030?” But then, the COVID-19 virus arrived and proliferated throughout the world, creating disruptions at every step along its deadly path. Now things are very different than they were at the dawn of 2020. Every facet of our lives and our ecosystem, including industrial manufacturing has been dynamically altered. This paper, too, has been altered. Now it focuses on what we have learned in the past few months and where that knowledge will lead us as we emerge from beneath the shadow of an extraordinary viral pandemic – one that quickly revealed our strengths and weaknesses. Now we must abruptly change course and learn to operate under a new set of conditions. We must adapt to this new reality. And quickly. In this interactive white paper, Paul Galeski, founder of MAVERICK Technologies and one of automation’s most influential, knowledgeable and forward-thinking experts, shares his vision on Flexible Intuitive Technology (FIT) and the future of automation. Find out what FIT means to you and your organization, and how you can use this information to move forward in this ever-changing technological landscape. View the paper at: www.mavtechglobal.com/people-processes-pandemics-cfe-wpc/

info@mavtechglobal.com 888.917.9109 www.mavtechglobal.com

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ANSWERS

COVER STORY: WIRELESS NETWORKS Andrew Cureton, Emerson

Evaluating IoT wireless protocols for industrial applications Do IoT-based wireless technologies designed for municipal, building, and residential sensor networks offer anything for industrial users? How about 5G?

hen approaching technical evaluations, engineers must compare tradeoffs, such as weighing pros and cons of a Coriolis versus an ultrasonic flowmeter for a specific application, if a wired or wireless communication should be used, or what kind of wireless protocols to use. When making these types of comparisons, few situations are more complex than industrial wireless protocols due to the factors involved and the many possibilities. Looking at the list of industrial wireless protocols, each is optimized in a specific way to balance interacting characteristics. These include: • Power consumption: Wireless functions require power, but adding capabilities increases overall consumption, which reduces battery life. • Bandwidth: Sending a little data, such as one process variable, is easier to design and implement; increasing bandwidth increases power consumption.

Figure 1: WirelessHART was designed from the ground up to support field instrumentation. Images courtesy: Emerson

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• Update rate: How often must the device send its data? Once per day or six times per minute? Multiply sending frequency times bandwidth for total power consumption. • Distance: Increasing transmission distance calls for more power, but can be helped with effective antenna design and placement. • Bidirectionality: A device that only transmits data is simpler to design and less power hungry, but lacks many useful capabilities. • Reliability: Critical data requires a mechanism to verify transmission with a return acknowledgment. Or it must send the data again until it is successful. • Security: Only the intended recipient should be able to understand the data. Operation for an industrial wireless device is further complicated by its fixed location. Both ends of a communication are locked in place and can’t move around the side of the building for better reception.

Specific use case examples

Consider two functional extremes: a smartphone and a water-meter reporting device. The smartphone has every function imaginable: It’s bidirectional, has multiple radios and can handle large amounts of data, but reliability is low. Calls get dropped and downloading a file can be hit-or-miss. It’s up to the user to try again. A smartphone also has short battery life. The water-meter reporting device sends a very small packet of data, maybe only once a month, so the battery can last 10 years. It has no bidirectional capability so if the receiving system misses a transmission, it can try again tomorrow. Industrial applications fall somewhere between these two extremes and need to find the right balance. For more than 13 years, WirelessHART (adopted by the International Electrotechnical Commission as IEC 62591) has served industrial users as the most widely deployed wireless protocol for process instrumentation, actuators and other advanced field devices (Figure 1). It optimizes the tradeoffs discussed for this specific and critical application. WirelessHART is a secure, multi-vendor, interoperable wireless standard designed to provide www.controleng.com

highly reliable, low latency connectivity for process monitoring and automation applications. WirelessHART uses IEEE 802.15.4 radio technology with deterministic scheduling, plus frequency, temporal, and path diversity to achieve reliable, deterministic data transport using very little power. WirelessHART instruments have an expected 10-year battery life with update periods of 30 seconds and also supports low-latency downstream communications without sacrificing battery life, and it works with most existing handheld field devices to support calibration and diagnostics in the field. Its range is relatively short, but its self-organizing mesh technology can pass messages from one instrument to another, or via a repeater, to cover long distances or circumvent network disruptions. The network’s short range and low bandwidth extend battery life, even with frequent updates. The battery can also power a sensor, such as for measuring pressure or temperature, that likewise has low power consumption. This makes it possible to manufacture pressure, temperature and other transmitters as native instruments in a single unit, requiring no external wiring of any kind. Any HART-enabled instrument can be converted to WirelessHART by adding an adapter (Figure 2). Greater number of WirelessHART instruments has extended the catalog of process instruments and basic analyzers.

Extending wireless applications

Developing a wireless protocol and going through all the approval procedures is a time-consuming and expensive undertaking. Developers often explore other possible applications and customers before moving into the world of industrial instrumentation. Some more complex process analyzers create large amounts of data and use industrial Ethernet or Wi-Fi outputs, but energy consumption doesn’t suit battery power. Using an internet connection and an IP address for many individual instruments hasn’t caught on. Technologies emerging from the Internet of Things (IoT), such as LoRaWAN, are being promoted as ways to connect industrial, building automation and residential sensors. The LoRa Alliance describes the protocol: “LoRaWAN network architecture is deployed in a star-of-stars topology in which gateways relay messages between end-devices and a central network server. The gateways are connected to the network server via standard IP connections and act as a transparent bridge, simply converting RF packets to IP packets and vice versa. The wireless communication takes advantage of the long-range characteristics of the LoRa physical layer, allowing a single-hop link between the end-device and one or many gateways. All modes are capable of bi-directional communication, and there is support for multicast addressing groups to make efficient use of spectrum during tasks such as firmware over-the-air upgrades or other mass distribution messages.”

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COVER, Figure 2: Instruments can be native WirelessHART (left) or have a wiredHART-to-wireless adapter added.

A handful of industrial LoRaWAN devices have come onto the market with a range of one mile. That coverage, however, comes at the expense of battery life with the devices promising just four years, even with an update rate of one hour. The long update rate limits this approach to monitoring of very slow-moving process variables.

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5G beyond smartphones

5G is reaching

the industrial Now 5G is reaching the industrial world. Most general discussion centers on enhanced mobile broadworld. band (eMBB) for smartphones and tablets, with major improvement as compared to 4G for virtual reality media and UltraHD video. eMBB supports data rates up to 20 Gbps with up to 10,000 times higher traffic than 4G systems. Two other areas have drawn industrial users’ attention: 1. Ultra-reliable low-latency communications (URLLC) provides support for critical systems requiring extremely low latency, such as self-driving vehicles and machine control. URLLC offers transport latency of less than 1 ms with data rates up to 10 Mbps. KEYWORDS: WirelessHART, Like eMBB, URLLC relies on the use of the process instrumentation, field instrumentation, 5G 5G New Radio (5G NR). Developing a wireless protocol for 2. Massive machine-type communiprocess instrumentation applications cation (mMTC) supports machine-tois a time-consuming and expensive machine communication with data rates undertaking. up to 100 kbps. mMTC applications Technologies emerging from the include municipal metering and smart Internet of Things (IoT) are being promoted as ways to connect city deployments where extremely low industrial and building automation data rates (such as once per day or less) sensors. and high latency are acceptable. No single wireless protocol solves Many equipment vendors and end all application problems and that will user companies look forward to the coexcontinue even in the 5G era. istence industrial protocols such as WireONLINE lessHART with 5G and possibly other Click on headline of digital edition to wireless protocols to transform the future see more images and read “Options of global industry. ce for backhaul” with this article online.

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Andrew Cureton, pervasive sensing product manager, Emerson. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

www.controleng.com/ networking-and-security/wireless/

CONSIDER THIS What benefits could your manufacturing facility gain from wireless protocols?

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ANSWERS

COVER TOPIC: WIRELESS Richard Kluth, Siemens Industry Inc.

Four best practices for industrial wireless LANs Industrial wireless local area networks (LANs) require attention to site survey, lifecycle management, cybersecurity and collaboration.

ince the IEEE 802.11 “Wi-Fi” standard debuted in 1997, most modern industrial enterprises today have some, if not most, of their operations running over industrial wireless LANs (iWLANs). These are the communications backbones of Industry 4.0 production and logistic models and the Industrial Internet of Things (IIoT) concepts and technologies they employ. (Haven’t set up industrial wireless, yet? See this August Control Engineering article: “What industrial wireless network should I use?”) Unlike Wi-Fi for home, office, and public spaces, iWLAN applications have loftier performance requirements, which may include low-latency, deterministic, ultra-reliable, highly secure, and/or accommodate complex radio frequency (RF) environments. After all, production output, damage to machinery or equipment, and even environment, health, and safety (EHS) concerns may be at stake. If an application involves roaming vehicles or robots (see related articles in this issue), the iWLAN’s complexity is much greater. All of that’s a tall order, which is why iWLANs require a clear understanding of an application’s requirements and then careful RF engineering to meet those requirements.

Best practices for iWLAN use

Although entire books are available on the RF engineering needed for proper iWLAN deployments,

COVER inset: Figure 1: This example map simulates wireless coverage and attenuation in a facility, showing where coverage is reliable and where it’s not. Graphics courtesy: Siemens Industry

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these four best practices can provide industrial enterprises with a broad framework to ensure they get the most out of iWLAN investments over time.

1. Site survey for industrial wireless

Industrial environments typically have diverse sources of RF interference, reflections, and absorption that can cause varying degrees of attenuation and irregular radio-wave patterns. Among them: Nearby office WLANs, adjacent iWLANs, and RFID systems; metal machinery, ductwork, racks and doors; and thick concrete walls, liquids and even people. That’s why a thorough site survey is critical for successfully deploying and operating an iWLAN. A site survey’s goal is to ensure adequate signal coverage for the target facility and application via the optimal placement of wireless access points (APs), correct channel selections and the best signal-to-noise ratios (SNRs) possible between APs and client devices. To conduct a site survey, you’ll need the facility’s floor plan, the application’s data-rate requirements, and tools to measure SNR and detect other RF sources, such as wireless site survey software and an RF spectrum analyzer. If these tools are not available, experts with professional software and extensive site survey experience are available to assist in this stage of iWLAN design. Next is a visual inspection of the facility to validate the floor plan because significant modifications may have been made to the original structure and note any possible attenuation sources that may be free-standing, such as inventory or equipment racks, machinery and stock. A spectrum analysis also should be performed with appropriate tools. At this point, preliminary AP locations can be determined, outfitted, and tested. Elevated locations, such as building columns (indoor) or posts (outdoor), rafters, and ceilings, are ideal. AP devices with multiple and omni-directional antennas can provide optimal RF coverage and are best connected with POE (power-over-Ethernet) cabling. In configuring APs and client devices, just get them “talking” to each other. They should be placed www.controleng.com

so their radio waves create cells of coverage that lightly overlap to ensure no signal gaps or channel overlaps. After a radio connection is established, enable higher-level features, such as security and roaming capabilities, one at a time, so each can be troubleshooted, if needed, as they’re added. Same goes for tweaking connections, such as adjusting transmit power or roaming strategies. And don’t forget to document everything.

2. Lifecycle management

Properly designing, engineering and configuring an iWLAN can be quite an undertaking. It often requires outside RF engineering expertise if not available inhouse. While it’s possible to set one up correctly using a do-it-yourself, learn-as-you-go approach, doing so can consume a lot of skilled information technology/operational technology (IT/OT) resources that might be needed elsewhere in the industrial enterprise and take much more time and effort than hiring experts for a short-term engagement. Either way, once an iWLAN is up and working as planned, the tendency to “set-and-forget” wireless network operation can be a mistake. Like any piece of production machinery, the iWLAN becomes a valuable plant asset that needs to be managed over its lifecycle. It’s a good idea to review the site survey, too. Why? Because over time, a facility’s floor layout can change substantially. For example, if a new machine or a metal rack is installed, or even a wall or partition, it can change the production environment’s RF characteristics, either a little or a lot. If it’s a lot, the iWLAN could stop working altogether. If it’s only a little, the iWLAN’s performance could be running sub-optimally and increase the risk of communication faults, leading to downtime or other costs. As part of the periodic site survey, an updated spectrum analysis should be performed as well. New internal systems or even neighboring buildings will be commissioned with Wi-Fi. These new systems which were not present in the original spectral analysis, may intrude on or increase the density of the wireless spectrum to the detriment of the existing wireless solutions. It’s worth remembering a spectrum analysis is only a snapshot in time, and therefore should be routinely monitored. If a new spectrum analysis reveals significant changes that affect the existing wireless systems, adjustments should be made (either to the new systems or existing systems) so all systems can co-exist and run optimally. In addition to changes in floor layouts, available iWLAN and wireless technologies will inevitably evolve over time, just as IEEE 802.11 has evolved, and now 5G wireless is emerging. Plants using older Wi-Fi technology could be incurring opportunity costs, as newer wireless technologies can provide more capabilities, often at less cost.

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Figure 2: To troubleshoot glitches in industrial wireless reliability, perform a follow-up spectral analysis and reveal new sources of radio energy.

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It’s a good idea to review the site survey, because over time, a facility’s floor layout

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can change substantially. 3. Cybersecurity

Industrial enterprises – especially critical infrastructure, such as power plants, oil and gas operations, transportation and others – are big targets for cyber threats. Consider ransomware. It can encrypt the code or data needed to operate machinery or a process and demand a payment to unencrypt it. More and more, its perpetrators are targeting big industrial operations because the payouts can be much larger than individuals or small-to-medium size businesses. The KEYWORDS: Industrial wireless, good news is iWLAN hardware comes wireless reliability with built-in security features, such as Industrial wireless site surveys firewalls, encryption, and device authentican change over time and may need revisiting. cation. Add-on hardware can provide virConsider cybersecurity of tual private network (VPN) capabilities. industrial wireless networks. In some components, these features will Collaboration of IT/OT teams need to be configured in deployment, a helps with industrial wireless step that can be overlooked and leave the reliability. iWLAN vulnerable to intrusion. CONSIDER THIS For more cybersecurity, details Time to revisit wireless reliability on 4. IT/OT collaboration, and is before there’s an outage. The advice on improving reliability of attenuation map can change over existing industrial WLANs, see this artitime. cle online. ce

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ONLINE

Richard Kluth is network solutions consultant at Siemens Industry. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com.

If reading from the digital edition, click on the headline for more resources. www.controleng.com/magazine www.controleng.com/ networking-and-security/wireless/

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ANSWERS

COVER TOPIC: WIRELESS Kerstin Wolf, R. Stahl

Explosion protection for industrial wireless networks Wireless Ethernet in hazardous areas works with explosion-proof enclosures.

perators of process technology systems seeking to implement wireless device communication in hazardous areas have had few options to choose from when selecting suitable access points because most industry-specific models with Ex approval do not offer high-performance, central network management. Products with this feature often are not suitable for industrial environments. An option is integration of standard devices without Ex approval in explosion-protected enclosures. Compact high-frequency isolators for intrinsically safe wireless signal conversion make it possible to use standard antennae in Zones 1 and 2. Since wireless networks became an integral component of Industry 4.0, particularly in logistics, machine maintenance and repair, and production monitoring and control, wireless communication networks have been on the rise in the process industry, too. The advantages of this technology are apparent in temporary installations, when equipping sensors and actuators in remote plants, and in process control with mobile end devices. One relevant application is asset management, where wireless measured value determination can simplify the process of monitoring the condition of production equipment and improve predictive diagnostics.

Figure 1: Wi-Fi can be used in hazardous areas for wireless data communication with high bandwidths. R. Stahl offers wireless (Wi-Fi) hardware enclosures and suitable HF isolators in Ex Zones 1 and 2. All images courtesy: R. Stahl

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Industrial wireless standards

Different wireless standards meet industrial requirements and offer different advantages depending on the intended purpose. For many static sensor/actuator connections with low data volumes, sub-GHz or 2.4 GHz technology such as WirelessHART, ISA100.11a Wireless Systems for Automation, LoRaWAN from LoRa Alliance or Low Energy Bluetooth are ideal. For communication with separate production units or mobile devices, technology such as 3G, LTE, NB-IoT or, in the future, 5G are more suitable. For fast, broadband data transmission between mobile participants over limited terrain, however, powerful Wi-Fi solutions in accordance with the IEEE 802.11 Ethernet protocol series of standards are the solutions of choice. They enable the interruptionfree transfer of network participants within from one access point to another within production areas.

Wireless challenges, hazardous areas

However, since all wireless-capable equipment, such as Wi-Fi access points, mobile wireless components and radio frequency identification (RFID) readers and antennae, always represent potential ignition hazards according to applicable explosion protection directives, suitable ignition protection that includes the interfaces is required to enable use in hazardous areas. Electromagnetic fields cannot, as a rule, ignite an explosive mixture as long as the ignition source does not work with a transmission power of several hundred watts. There is a risk the fields could induce currents in insufficiently EMC-protected metal items or electronic switches and these currents could cause sparks to form. As a result, the international standard IEC EN 60079-0 defines limiting value requirements for wireless signals in hazardous areas. As well as compliance with limiting values in normal operation, this standard requires a malfunction inspection for devices in Zone 1, which accounts for the explosion hazard posed by short circuits, shunts or interruptions. While the production industry has a wide range of suitable network components to choose from, process technology plant operators, who need to take www.controleng.com

Figure 2: Explosion-protected encapsulated wireless systems are available for Wi-Fi networking for Zone 1.

explosion protection into account when looking for suitable equipment, face unique challenges. The wireless components required for operation in Ex Zone 1 are mostly protected by special enclosures. To use standard Wi-Fi devices without special approval in hazardous areas, they need to be installed in a flame-proof enclosure or an enclosure encapsulated using an overpressure; this enclosure must meet the requirements of the Ex d or Ex p type of protection. However, this does not address the issue of antennae because most encapsulated enclosures of this kind are made of metal, which blocks the majority of the electromagnetic radiation from the antenna. With a directional antennae, RFID readers or motion detectors can be positioned in Ex d housings behind a glass pane. With omnidirectional radiation, a limited number of Ex-certified external antennae have been available. The designs, which are determined by the need to provide explosion protection, do not include the standard plug connectors needed for making installation and maintenance easier.

Safely connect to antennae

Based on a comprehensive range of enclosures and suitable HF isolators, solutions for use of conventional Wi-Fi hardware in Ex Zones 1 and 2 are available to accompany conventional antennae. To safely encapsulate potentially hazardous Wi-Fi access points in Zone 1/21, the explosion protection expert has produced flame-proof enclosures with projectspecific dimensions for devices from manufacturers like Cisco and Aruba with Ex d type of protection. A separate connection chamber with Ex e protection enables CAT cables or fiber optics to be connected easily and quickly. This is also enabled by the Ethernet terminals and FO splice cassettes for use in Zone 1. The enclosures have been tested and certified for global use have intrinsically safe bushings that allow the external connection of any industrially-capable antennae. Compact, high-frequency isolators are available for frequency ranges from 150 MHz to 8 GHz; these isolators support Wi-Fi and a range of other wireless standards such as Bluetooth,

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Figure 3: Diagram shows Division 1 and 2 use of wireless devices and how the HFisolator prevents the transmission of low-frequency signals.

LoRaWAN, WirelessHART and ISA100.11a. The high-frequency isolators prevent low-frequency signals from being transmitted in case of an error and ensure the external antennae only receive signals at an intrinsically safe level in accordance with Ex ia IIC. Such solutions are easier to install and handle because the wireless devices and external antennae don’t need to be connected using inflexible wires without plug connectors. Instead, they use preassembled connecting cables that can be connected and disconnected when necessary. An easier option is attaching the antenna to the enclosure bushing.

Advantages of standard use

Many companies already use Wi-Fi networks in administration and logistics departments. Since the advantages of these wireless networks are greatest when they are standard throughout the company and can be managed centrally, IT specialists typically recommend using consistently coordinated hardware. Another hurdle is avoiding additional work and expenditure caused by changing supplier or by changes to maintenance strategies – a transparent aim. As a result, many users are keen to use identical devices in their production areas when extending their Wi-Fi network. IT devices generally do not have Ex approval; in the past, this has meant these devices had to be additionally protected by an Ex d enclosure for use in Zone 2.

Kerstin Wolf is press information officer, R. Stahl; Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com.

M More ANSWERS

KEYWORDS: Industrial wireless, hazardous area Wi-Fi enclosures Hazardous area zone 1 and 2 limit wireless communication options. Suitable enclosure and connectors widen areas commercial IT technologies can be used in Zones 1 and 2. CONSIDER THIS With the right enclosure, would you consider using wireless in a hazardous environement?

ONLINE From the digital edition, click on the headline for more on new enclosures for Zone 2. www.controleng.com/magazine www.controleng.com/ networking-and-security/ wireless/

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ANSWERS

COVER TOPIC: WIRELESS Caitlyn Caggia and Daniel Browning, PDF Supply

RFID improves precision, efficiency in auto production Radio frequency identification (RFID) uses wireless sensors to passively tag objects with digital information; applications are helping automakers.

he automobile manufacturing market favors rapid customer responsiveness in a competitive environment. Production leaders have started exploring ways to identify, catalog and deliver materials using radio frequency identification (RFID) to enable faster assembly of customized products.

What are automaker challenges?

Manufacturers in the automotive industry launch new models frequently, which has shortened both product lifecycles and the timeline of upgrades. Producers rely on mixed-mode assembly production lines to provide the high level of personalization and customization consumers crave. These mixed-mode assembly production lines require a dynamic staging process, with a variety of suppliers delivering a high volume of diverse materials on short notice. Manufacturers face cross-cutting challenges in their supply chains, logistics and production management. Mixed-mode assembly for vehicle manufacturers is very manual and reactive to changes in the queue. Material delivery is driven by a predetermined daily material plan and by pulling material consumption from assembly sites. As buffer stock arrives to replenish stock to sufficient levels, workshop internal stock will request additional material based KEYWORDS: RFID, automotive, on manually reported needs; workers automation, material delivery, on the assembly floor will then identify assembly, production each vehicle, determine required materiRFID can address some als and deliver necessary supplies to their automotive production respective stations by hand. Many steps challenges. depend on workers’ input and effort, Other automation technologies can make RFID more effective. potential risks include high labor costs, RFID weaknesses can be operational inefficiency, lack of agility addressed (see table). and human error. In an effort to mitigate assembly line ONLINE risks, manufacturers employ lean producwww.controleng.com/ networking-and-security/wireless tion methods, including just-in-time (JIT) practices, manufacturing resource planCONSIDER THIS ning (MRP II) practices, waste reduction, How could RFID applications process standardization, and an emphaautomate other industries?

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sis on defect-free production. Industry efforts tend to focus on process improvements that decrease variability and reaction times. Auto manufacturers are researching applications of low-cost technology upgrades that can adjust to material delivery needs.

What is RFID?

Radio frequency identification (RFID) uses wireless sensors to passively tag objects with digital information such as production codes, bill of material (BOM) lists and other key identifiers. This information can be scanned into a larger network when the RFID tag is within proximity to an RFID reader which enables tracking of products, tools, people and workflows. RFID tags have become low cost and low power through extensive industry research and development efforts – some RFID sensors don’t even require a battery to supply power. For more background on RFID products and research, see Control Engineering’s article on RFID capabilities, “What is radio frequency identification? What can RFID products do?”

How can RFID help?

An RFID-based mixed-mode assembly buffer material delivery method is capable of providing the proactive insight vehicle manufacturers seek. Installing RFID tags on each vehicle, RFID tags on each material transfer container, and RFID sensors on each assembly workstation would automate the identification of models, cataloging of required supplies, and delivery of necessary materials. On average, mixed-mode assembly lines simultaneously produce 20-30 vehicle models. Many auto manufacturers currently use barcodes to streamline model identification. However, barcodes still require manual scanning as a vehicle enters and exits each assembly station. RFID sensors at each workstation can automatically identify each model and its location in the assembly line. Based on data from the Automotive News Data Center, each vehicle model, on average, requires roughly 1,200 materials; some models require up to 2,300 material types. Production buffer material www.controleng.com

What are potential weaknesses of RFID? RFID weakness

Potential RFID solution

COST: RFID systems may be more expensive then alternatives (such as barcode stickers or manual indentification

Lifetime cost of RFID systems may balance out against efficiency improvement and error reduction

SECURITY: Data is broadcasted over radio frequencies, which could be intercepted by outside parties

Encryption protocols are availble, like the NIST Advanced Encryption Standard (AES)

RELIABILITY: Environments the are dense with metallic objects, like factories, may be particularly susceptible to signal interference

Sensor integrity can be improved by using software to correlate multiple signals in an area from additional sensors

Table: Radio frequency identification (RFID) weaknesses and potential solutions are shown, including cost, cybersecurity and reliability Courtesy: PDF Supply

racks store various – but limited – quantities of these materials, which are replenished from larger internal stock rooms. Determining which vehicles need what materials at each workstation can be challenging. Along with model information, RFID tags can hold a bill of materials for a specific vehicle configuration and will automatically queue requests for upcoming material delivery needs. The facility inventory list will automatically update to reflect this new material consumption. By automatically updating both material consumption and material delivery in real time, error in material demand is virtually eliminated.

RFID enables material delivery

As materials need to transition from storage facilities to the assembly line, there is a delay between submitting requests and receiving material. A key benefit of RFID systems is their ability to optimize material delivery based on lead time. Lead time of delivery between storerooms and workstations is the sum of material preparation time, online time and safety time. Material preparation time includes gathering, packaging and loading supplies. Online time includes transport and unloading time. Safety time is a buffer reserved for emergencies and unexpected situations. Material is pulled from storerooms when the buffer inventory balance is less than the demand within one lead time. Some items are too large or heavy to be stored on workstation buffer racks, so these must be delivered before they are needed. In buffered and JIT deliveries, material transit containers are tracked through their RFID tags to confirm on-time delivery of necessary supplies to the correct location.

Maximizing RFID benefits

Software can integrate RFID capabilities with existing production management tools. Software will provide a user interface to monitor the production status of a mixed-model automobile assembly plant, monitor buffer material consumption, and coordinate buffer material demand and delivery. Sophisticated software can optimize parameters to:

www.controleng.com

‘

A key benefit of RFID systems is the ability to optimize material delivery based on lead time; material demand errors are

’

virtually eliminated.

• Increase fidelity of lead time calculations • Meet safety constraints (like maximum weight) of material transit containers • Optimize (un)loading order of material transit containers • Eliminate redundant input between RFID sensors by manipulating data acquisition/fetch cycles • Initialize material requests with RFID sensors recording vehicles before they reach assembly workstations.

What are potential weaknesses?

While RFID systems offer many advantages in automotive assembly, there are some weaknesses in RFID technology (See related table), such as cost, cybersecurity and signal interference. Current research has prototyped standalone RFID-based material delivery methods and accompanying software for mixed-model automobile assembly. Additional research and development to integrate RFID systems with existing enterprise systems would be required before RFID applications become widely accepted. ce

Caitlyn Caggia is a content writer and Daniel Browning is business development coordinator at PDF Supply. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. control engineering

October 2020

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ANSWERS

ARTIFICIAL INTELLIGENCE, MACHINE LEARNING Kevin McClusky, Inductive Automation

Exploring the benefits of AI and machine learning Artificial intelligence (AI) and machine learning (ML) can offer many benefits for manufacturers and provide positive outcomes with optimization, predictive maintenance and more. See three initial steps to every machine-learning project.

ust a short time ago, machine learning (ML) and artificial intelligence (AI) were fairly new concepts for a lot of people in the manufacturing industry. Many companies are getting up to speed and adopting these technologies slowly and methodically. Fortunately, offerings in this space have drastically improved in recent years. The basic concepts, however, are the same. Artificial intelligence (AI) is a catch-all term that covers any technologies where a computer or system seems intelligent. This can be anything from image recognition to airplane autopilot systems, which started appearing way back in 1914. Machine learning (ML) is a subset of AI and is focused on a machine’s ability to extract data insights. The study of machine learning is often about common ML algorithms, which are used to develop insights around data.

Four machine learning outcomes, benefits for manufacturers

In recent years, there’s been a change in focus from ML algorithms themselves to ML result categories. Here are the four main types of ML outcomes a lot of folks are seeking today: 1. Predictive failure and alarming can be one of the most significant areas of cost savings for a company. If a manufacturing line is about to go down, it can be much more cost-effective to shut

Machine learning and artificial intelligence GOAL

ALGORITHM

Predictive failure and alarming

Clustering, regression*

Process optimization

Regression*

Anomaly detection

Neural networks, clustering*

Defect analysis

Neural networks*

*Many alogrithms can accomplish these goals, but this should provide a good starting point.

Many algorithms match machine learning and artificial intelligence goals, but these can help a user begin. Courtesy: Inductive Automation

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October 2020

control engineering

it down early and do maintenance than for it to go down midstream. Predicting failures and generating alarms based on algorithms can save a lot of money. 2. Process optimization is another popular area. This normally takes the form of letting a system provide recommendations for tuning setpoints and variables for systems. There are two main types of process optimization: Open-loop and closedloop. Open-loop optimization involves user interaction, where the system may recommend changes to optimize a process, and an engineer or other expert reviews the recommendations and chooses whether to apply them. Closed-loop optimization takes any human intervention out of the process entirely and tuning recommendations are automatically applied. Often, a company will run an optimization algorithm in an open-loop fashion, and after becoming comfortable with the results, will then switch to closed-loop. Although closed-loop optimization is normally only done once every few minutes, hours, or days, there are a few cases where running faster may be useful. This would be to provide constant tuning of a process where tuning with traditional means, like a proportional-integral-derivative (PID) loop, may be very difficult or impossible. Most people who do high-speed ML process tuning end up running the ML algorithms for the tuning on an embedded PC or similar hardware right next to the PLC. 3. Anomaly detection finds deviations from normal operating conditions. This can provide insight into when a process is running sub-optimally, or sometimes this can predict bad production runs or mistakes by equipment. These systems generally provide a number indicating how close to normal operating conditions a process is. Results can be considered as an additional “sensor,” and the results of the anomaly detection algorithm can be used for anything from alarming on an abnormal condition to shutting down a process. Although www.controleng.com

similar to predictive failure and alarming, anomaly detection provides information about how a process is doing right now, rather than predicting how it may be doing in the future.

1. AI software vendors

4. Defect analysis is normally done through image-recognition algorithms and can be very useful for classifying parts and detecting abnormalities.

2. Low-code or no-code ML platforms

Three initial steps to every machine-learning project

While all this all sounds great in words, how can someone begin? Here are some initial steps that apply to every ML project: 1. Identify a system that could benefit from one of the outcomes mentioned above. 2. Define what you want to analyze with that system and what results you’re looking to achieve. 3. Verify you have plenty of historical data collected for that system. Most ML algorithms take mountains of data to be effective (to “learn” from). If you don’t have that available on the system, add historical logging and revisit in a few weeks or months.

Three steps to make a machine-learning model

The next step is generating a machine-learning model. Models are the programs that are executed to get needed results. To generate a model: 1. Prepare the data. All ML models need data to be generated. Most data exported from a historian or database isn’t perfect and needs to be cleaned. This normally means tossing bad rows, identifying data from bad sensors and excluding it and making sure the data looks reasonable. A process engineer familiar with the system can look at the data and help determine if it’s a clean data set. 2. The model needs to be trained. This is done by feeding it the cleaned data and choosing some training options. 3. The model is then scored to see how well it works. A model will always be generated if you go through training, but the model may be very bad at predicting things. Look at the score to see how well a model does and make sure the model scores well.

Three types of AI/ML software

While users might know how to prepare the data, they might not have any idea how to train a model or score it. If that’s the case, it’s time to choose AI/ML software. There’s a wide range of options.

www.controleng.com

A lot of companies have popped up here, with many who will build models, or offer pre-built models for certain types of equipment. A growing number of platforms help people begin creating their own models. Google Cloud AutoML, AWS ML, and Microsoft Azure Machine Learning Studio are examples. A basic knowledge of machine learning algorithms is normally suggested, but getting started isn’t too tricky. A little reading on clustering, regression and classification algorithms is a good starting point for beginners.

‘

Machine learning is available and accessible to anyone who wants to try this new technology

’

and pursue the promise it brings.

3. Coding ML platforms

These are the most common. If you have a data scientist or an information technology (IT) department already doing ML, you may just lean on them to point you in the right direction. Google, AWS, and Azure all have offerings. Additionally, many free options, like TensorFlow and Scikit-learn, can be run locally. There’s nothing wrong with starting with a lowcode ML platform and moving to a full coding platform later. The coding platforms are more complex to use and are often used by data scientists, but the added flexibility often leads to higher scoring models. The better the model, the better the results, and the more likely stated objectives can be achieved. KEYWORDS: artificial After initial exploration, many comintelligence, machine learning panies hire a data scientist to help with Artificial intelligence (AI) and ML efforts, but it isn’t required. These machine learning (ML) are being technologies are available and accessible used more by manufacturers as to anyone who wants to try this new techthey realize their benefits. nology and pursue the promise it brings. AI/ML software can help spot inefficiencies and improve There’s no question AI and ML will manufacturing operations. continue to be an important part of the There are many types of AI/ controls and automation landscape in ML software, and not all require the future. Companies looking for an a data scientist, though a strong advantage in operating costs or effiunderstanding of the data is ciencies may find a little research lookrequired by the person using it. ing at possible options to be well worth ONLINE their time. ce Learn more about AI and

M More ANSWERS

Kevin McClusky is co-director of sales engineering at Inductive Automation. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

machine learning at www.controleng.com.

CONSIDER THIS What applications on your plant floor would benefit from AI/ML software?

control engineering

October 2020

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ANSWERS

ARTIFICIAL INTELLIGENCE/MACHINE LEARNING Takayuki Sugizaki, Yokogawa Electric Corp.

Automated anomaly detection can maximize production Industrial artificial intelligence/machine learning (AI/ML) software and longrange sensors predict maintenance requirements, increase productivity, profits.

or product manufacturers, profitability requires safely maintaining efficient operation, minimizing expenses, and reaping as much production from plant equipment as possible. When plants are new, efficiency and reliability are not among highest concerns, but as equipment ages, unanticipated failures and the resulting reactive repairs often become problematic. Plant personnel can address this issue by accessing equipment data to identify problematic equipment requiring maintenance. The problem is manually analyzing data to generate insights requires a great deal of time, expertise and operational knowledge. Custom artificial intelligence/machine learning (AI/ML)enabled software removes most manual data analysis. The problem is this type of software can be cost-prohibitive to implement and maintain. Off-the-shelf AI/ML software, such as data logging software, deliver automated anomaly detection and alerting, empower personnel to identify equipment issues before failures occur. This software often serves a second purpose as a centralized repository for multiple devices’ data, including data captured by an Industrial Internet of Things (IIoT) long-range wire-

Figure 1: An AI package has a sensor, software to collect and analyze data, and a recorder to visualize data. Figures courtesy: Yokogawa

•

less sensor system. These types of off-the-shelf AI/ML software solutions increase operational efficiency, prevent equipment problems, and allow plants to plan maintenance prior to equipment failure.

Three development goals for AI-enabled products

An off-the-shelf AI/ML software solution prioritizes achievement of three goals:

October 2020

control engineering

1. Maintain healthy conditions in equipment: Log all equipment output, including deviations from the baseline. 2. Anticipate equipment maintenance via abnormal sign detection: When equipment variables begin to stray from normal values, the software flags this behavior and alerts personnel to take maintenance measures prior to failure. 3. Make AI accessible: End users do not need to be AI experts or contract expensive consultants to configure the software as it is usable by personnel of many skill levels. In the industrial automation space, a complete AI package includes sensors and controllers to gather data, PC-based software to organize and automatically analyze the data using AI/ML, and a recorder to visualize data (Figure 1). AI/ML software orients personnel to normal operational levels based on past performance and notifies them when an anomaly is detected.

Anomalies, predictive maintenance

Warning, alerting, and alarming are nothing new by themselves, as control systems often possess user-adjustable high, low, deviation, and outof-range setpoints. When equipment data exceeds the applicable setpoint, the user is notified. Automated anomaly detection offers significant improvements beyond these automated functions.. When AI-enabled software is first deployed, it undergoes a learning phase during which it monitors the equipment conditions to build a baseline for known, normal operation. Time dedicated to the learning phase varies by application and can be user-defined. Once the learning phase is complete and a normal operation baseline is established, the AI/ ML software begins automatically monitoring the equipment, build reports for personnel review and generate anomaly alerts when operation deviates from its baseline. These alerts provide users advance warning of potential equipment problems. AI/ML software should offer ease of configuration and user-friendly parameter changes, simwww.controleng.com

plifying these and other types of adjustments. The degree to which current equipment data matches the normal baseline can be given as a health score, signaling advance notice of an abnormality if something is negative (Figure 2). In addition to identifying abnormalities before equipment failures occur, AI-enabled software is ideal for creating predictive maintenance forecasts. As equipment data deviates from the baseline, AI/ML software shows a declining health score. Maintenance operators can use this indication to determine timelines for scheduled equipment maintenance prior to potential failure. These automated visual insights free up time for tasks besides data analysis and maintenance scheduling.

AI-enabled software functions

Many software suites can wrangle data from multiple devices and measurement points on a network into one location, allowing users to visualize the data using graphs and charts. However, data visualization alone does not inform on the normalcy of the data being viewed. To deal with this problem, AI/ML software employs algorithms that automatically overlay trends and highlight anomalies on the graphical user interface. With certain types of equipment data, such as temperature or pressure, it is often possible, if not always ideal, to use classical methods of high or low limit setpoint warnings to detect an abnormality. However, for data such as motor speed or the vibration of a machine, it is difficult to understand if the equipment data is normal without manual and meticulous historical data analysis. This manual analysis is time-consuming and requires a skilled data analyst with extensive expertise, but these experts are in short supply. AI-enabled software transforms this tedious manual process into instant automated insight, comprehensible by all staff. AI/ML software uses a clustering algorithm to detect anomalies. This means anomaly detection is not limited to data following linear, quadratic or other basic functions. Advanced AI-enabled software creates correlations among multiple equipment data points when it determines a normal baseline, and detects when the relationships are abnormal during operation. Automatic anomaly detection frees personnel from manual analysis, and it often detects problems missed by data examined using visualization tools such as charts and graphs. This typically results in increased uptime and lower maintenance costs.

Long-range sensors, data

In the age of IIoT, it is not practical to limit data collection to stationary and accessible processes and areas. Applications like mobile machines and vehicles, rural outpost facilities, and lengthy pipelines generate data worth gathering and analyzing, but also present obstacles regarding non-stationary, long-range and low power data transmission.

www.controleng.com

Figure 2: In an example of anomaly detection, an AI/ML software’s health score indicates an abnormality prior to equipment failure.

Low power-wide area network (LPWAN) systems address these and other issues and are being used across many enterprises to capture data in difficult applications. A few noteworthy characteristics of LPWAN sensors are: • They consume minimal power, hence batteries can last several years • They provide long-range wireless data transmission, capable of transmitting data over several miles • Highly efficient communication protocols, resulting in lower data usage requirements than 3G, 4G, and 5G. Each sensor measures vibration, temperature, or pressure, delivering monitoring capabilities for equipment located just about anywhere (Figure 4). A long-range wide-area network (LoRaWAN, a subcategory of LPWAN) enables a connected enterprise without the necessary provisions for wired lines or IEEE 802.11 Wi-Fi. Data transmitted by the sensors can KEYWORDS: artificial intelligence, be supplied to host systems, includmachine learning, IIoT ing AI-enabled software. By using the AI-enabled software for analysis wireless sensors in conjunction with AI and anomaly reporting can help detect problems before they cause anomaly detection software, plant perdowntime. sonnel receive a clear window into the Detecting anomalies can help plant operational health and maintenance personnel focus their maintenance needs of equipment, no matter where it efforts on the issues that need the is located. most attention and allow their assets to have a longer life. AI-enabled software capability can add years to an asset’s life, avoiding failures and ONLINE costly repair or replacement. ce More figures, more about anomalies

M More ANSWERS

Takayuki Sugizaki is an IoT wireless promotion manager at Yokogawa Electric Corporation. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

and AI help with equipment replacement with this article at www.controleng.com.

CONSIDER THIS What parts of your plant would benefit from anomaly detection?

control engineering

October 2020

•

ANSWERS

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Mike Williams, Rice University

Address AI bottlenecks with machine-learning algorithms Improve data-intensive computer processing with machine-learning algorithms.

ice University researchers have demonstrated two methods for designing innovative data-centric computing hardware and co-designing hardware with machine-learning algorithms that together can improve energy efficiency by as much as two orders of magnitude. Advances in machine learning, the form of artificial intelligence behind self-driving cars and many other high-tech applications, have created a new era of computing – the data-centric era – and are forcing engineers to rethink aspects of computing architecture that have gone mostly unchallenged for 75 years. “The problem is that for large-scale deep neural networks, which are state-of-the-art for machine learning today, more than 90% of the electricity needed to run the entire system is consumed in moving data between the memory and processor,” said Yingyan Lin, an assistant professor of electrical and computer engineering. Lin and collaborators proposed two complementary methods for optimizing KEYWORDS: artificial data-centric processing. Both were presentintelligence, machine learning ed June 3 at the International Symposium Two methods for designing on Computer Architecture (ISCA), a coninnovative data-centric ference known for new ideas and research computing hardware with in computer architecture. machine-learning algorithms that together can improve The drive for data-centric architecture energy efficiency. is related to a problem called the von NeuThe drive for data-centric mann bottleneck, an inefficiency that stems architecture is related to the von from the separation of memory and proNeumann bottleneck, which cessing in the computing architecture that stems from the separation of memory and processing in the has reigned supreme since mathematician computing architecture. John von Neumann invented it in 1945. These methods are designed By separating memory from programs and to reduce the bottleneck and data, von Neumann architecture allows a create better efficiencies. single computer to be incredibly versatile; ONLINE depending upon which stored program is See additional artificial loaded from its memory, a computer can be intelligence and machine used to make a video call, prepare a spreadlearning stories at www. sheet or simulate the weather on Mars. controleng.com. Separating memory from processing also CONSIDER THIS means that even simple operations, like addWhat advances do you ing 2 plus 2, require the computer’s procesanticipate for the future of sor to access the memory multiple times. artificial intelligence and This memory bottleneck is made worse by machine learning?

M More ANSWERS

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October 2020

control engineering

massive operations in deep neural networks, systems that learn to make humanlike decisions by “studying” large numbers of previous examples. The larger the network, the more difficult the task it can master, and the more examples the network is shown, the better it performs. Deep neural network training can require banks of specialized processors that run around the clock for more than a week. Performing tasks based on the learned networks – a process known as inference – on a smartphone can drain its battery in less than an hour.

Machine learning hardware

“It has been commonly recognized that for the data-centric algorithms of the machine-learning era, we need innovative data-centric hardware architecture,” said Lin, the director of Rice’s Efficient and Intelligent Computing (EIC) Lab. “But what is the optimal hardware architecture for machine learning? There are no one-for-all answers, as different applications require machine-learning algorithms that might differ a lot in terms of algorithm structure and complexity, while having different task accuracy and resource consumption – like energy cost, latency and throughput – tradeoff requirements. Many researchers are working on this, and big companies like Intel, IBM and Google all have their own designs.” One of the presentations from Lin’s group at ISCA 2020 offered results on TIMELY, an innovative architecture she and her students developed for “processing in-memory” (PIM), a non-von Neumann approach that brings processing into memory arrays. A promising PIM platform is “resistive random access memory” (ReRAM), a nonvolatile memory similar to flash. While other ReRAM PIM accelerator architectures have been proposed, Lin said experiments run on more than 10 deep neural network models found TIMELY was 18 times more energy efficient and delivered more than 30 times the computational density of the most competitive state-of-the-art ReRAM PIM accelerator. TIMELY stands for “Time-domain, In-Memory Execution, LocalitY” and eliminates major continued on p. 47 www.controleng.com

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ANSWERS

HMI HELPS INDUSTRIAL INTERNET OF THINGS Ramey Miller, Siemens Industry

HMIs for IIoT optimization Unified human-machine interface (HMI) software centralizes device management and machine data, enhancing usefulness and increasing connectivity.

ncreasingly connected and higher-performing manufacturing plants require corresponding automation advances. While sensors and programmable logic controllers (PLCs) have become smarter over the last decade, not all human-machine interface (HMI) software experienced the same technological boosts. Now, though, the latest generation of HMI software has many advances including: • Improved graphical characteristics • Onboard productivity applications • The ability to connect to a wider variety of devices, and • The capability for users to define scripts and data pipelines on the HMI’s open platforms. These advances are optimizing industrial HMIs for use with devices in Industrial Internet of Things (IIoT) applications.

Improved operational technology

Digitalization is no longer a competitive advantage in manufacturing; it is a competitive imperative for profitability, longevity and responsiveness to evolving market trends. Unified HMI software meets these challenges by allowing developers to build sophisticated, responsive, and feature-rich applications suited for the digital age. This new wave of HMI software, running on dedicated devices or PCs, is more attuned with modern smartphones than with its clunky and antiquated predecessors. Modern unified HMIs ship with preinstalled applications for viewing documents, watching instructional media clips and accessing external web-based systems. Improved multitouch gestures – such as zooming and panning – allow for smooth document navigation and web browsing (Figure 1). Operators can use multitouch swipes to change screens and scroll within lists. Support for native web technologies like HTML5, scalable vector graphics (SVGs) and JavaScript is increasingly common. This functionality gives developers the ability to customize and animate HMIs, and the move from pixel-based to vector-based graphics improves on-screen aesthetics and machine visualization. Web server capability allows authorized opera-

www.controleng.com

Figure 1: Siemens WinCC Unified HMI devices provide support for multitouch gesture recognition, along with web technologies like HTML5, SVGs and JavaScript. Images courtesy: Siemens Industry Inc.

tors to remotely access HMI applications from any device capable of hosting a web browser – such as a laptop, smartphone or tablet – withoutinstall apps or plugins. This enables opportunities for collaboration between plant-floor staff and engineers in the office, making it easier for teams to troubleshoot issues.

HMI, IIoT collaboration, connections

For small machine shops and international enterprises alike, collaboration is vital to operational improvement. To encourage increased collaboration, unified HMI software supports sharing of screens, tags, alarms and production data among multiple devices on the plant floor, which stores all data at a central location. Modern HMI devices also support data transmission over multiple protocols such as message queuing telemetry transport (MQTT) for cloud connections. Regardless of the plant layout, it is advantageous for manufacturers to consolidate all production data for analysis and process improvement. In the past, each machine type often required its own third-party driver for data transfer to a central location, but unified HMI software overcomes this obstacle. Built on platforms such as the .NET programming language, unified HMI software system connectivity extends control engineering

October 2020

•

ANSWERS

HMI HELPS INDUSTRIAL INTERNET OF THINGS

Figure 2: Siemens WinCC Unified HMI software enables management of many devices. Scaling is simple due to a common object library.

ANSWERS KEYWORDS: HMI,

human-machine interface, Industrial Internet of Things This new wave of HMI software, running on dedicated devices or PCs, is more like modern smartphones. Collaboration is vital to operational improvement; HMI software helps. HMI software technology must keep pace with interconnections.

ONLINE More about security, future HMIs Learn more about HMI and IIoT systems www.controleng. com/IIoT www.controleng. com/HMI

CONSIDER THIS What challenges and considerations do you think are most important when it comes to HMI software?

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beyond plant-based historians to higher level organizational workflows in manufacturing execution and enterprise resource planning systems. Developers can leverage the integration of unified HMI software with these workflows to define rules and actions for business processes influenced by triggers from production data. It is also possible to monitor production key performance indicators and include this data in business process reporting. By installing optional apps within a unified HMI software environment, operators can receive production-related mobile alerts and notifications via a parallel app installed on their smartphone, smartwatch, or tablet.

Shared software ecosystem

The key to connectivity across the plant is the unified HMI’s shared software ecosystem. Shared software means one HMI development and runtime environment is used with all visualization devices – control room computers, smartphones, tablets and panel HMIs. All visualization interfaces share a common library of application objects, SVGs and scripts. Because symbols can be reused across device types, it is no longer necessary to spend time and money developing new graphics as plant production expands and additional visualization devices are brought online (Figure 2). Shared software advantages do not stop at the HMI level. Unified HMI software comes with its own suite of apps, which empower plant owners to modularly select and build out their software infrastructure to suit specific company needs. These apps provide business workflow integration, machine-to-machine (M2M) data exchange, data visualization and analysis, central device manage-

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ment, and other functions. Central device management – also known as group policy management – is a familiar concept for mobile devices, but it is less common among industrial HMIs. Unified HMI software introduces this capability to enable version control, security patching and app management on HMI devices across the enterprise based on administrator-defined device group rules and assignment. This ensures devices are only used for authorized purposes and protected against security vulnerabilities. For users, a shared software ecosystem means a similar look and feel across all visualization and control interfaces, including mobile devices. This leads to enhanced operator familiarity and better decision making because less effort is required to understand multiple interfaces, which frees up time to focus on operational improvements. A parallel and intuitive interface across devices also lends itself to less user frustration.

Openness for modern plants

On top of consistency throughout its own software ecosystem, unified HMI software gives users the ability to import custom controls and files. Developers can import objects created with third-party tools into the unified HMI software for deployment in runtime applications. The software’s openness also allows for exchange of large amounts of information with databases and other systems through the use of common .NET and C++ frameworks. Users also can create open application programming interfaces (APIs) for integration with business and production workflows. Machine builders and end users alike can inject their custom programming into the unified HMI software’s DNA as open APIs. For example, developers can create comparison reports and debugging traces to catch errors in application code or device configurations before they manifest, reducing commissioning time and mitigating machine malfunction risks. Application openness delivers the accessibility required to analyze data generated throughout the plant, without creating unnecessary inefficiencies or downtime. Runtime openness provides thirdparty apps with direct access to HMI runtime tags and custom web controls for increased equipment and workflow flexibility. Offline data collection tools are natively included for submitting data to a designated server. This allows for exchange of large amounts of information with database systems – as well as sharing screens, tags, event archives, and historical alarms. ce

Ramey Miller, HMI edge/product marketing manager, Siemens Industry Inc. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com. www.controleng.com

ANSWERS

PACKAGING AUTOMATION Don McLeod, Grantek Systems Integration

Automation helps packaging verification Machine vision and code readers verify packing label quality in a packaging automation system integration project. What will you see and learn?

utomation and controls can help packaging implementations and improve product safety and quality. Don McLeod, systems engineer at Grantek Systems Integration, answered questions from Control Engineering about a recent packaging automation project and offered advice on some of the lessons learned from the project.

Q. Can you please give a brief description of the packaging-related project? McLeod: Food packaging needed label verification to ensure packaging quality. In the application: • Artwork is pre-printed on packaging materials • Verification is required to ensure that correct packaging is being used for the product being produced • This is critical for facilities where there can be potential allergens in some products but not others. Q. What was the scope of the project and goals? McLeod: The goal was to ensure that all packaging and labels matched the product being produced. That included: • Inspection of tub for 2D code • Inspection of lid for correct graphic • Inspection of label for case for correct product 1D product code. Q. What types of automation, controls, or instrumentation were involved? McLeod: The project included programmable logic controller (PLC), human-machine interface, code readers, a machine vision camera, machine vision software and industrial Ethernet. The machine vision camera trigger was hardwired to the PLC but results were handled over EtherNet/IP industrial Ethernet from ODVA. Q. What were particular challenges outlined in the project? McLeod: Project challenges included installing equipment in tight spaces and ensuring 360-degree coverage of the industrial tub on the conveyer. www.controleng.com

Q. How were those issues resolved? McLeod: Camera coverage was ensured in the following ways. • We designed a stand that allowed for multiple Cognex DataMan cameras to be mounted looking at the side of the tub and one Cognex In-Sight camera with a light that looks down from above. • Six Cognex DataMan cameras were mounted to look at the side of the tube forming a circular array around the tubs as they travelled along the conveyor. This ensured that the 2D code would always appear in the field of view of the cameras. Q. Can you share some positive metrics associated with the project? McLeod: Two significant metrics were to avoid read rates for code readers less than 0.5% and to have zero false fails for lid Cognex PatMax software. Those were achieved. Q. What were the resulting lessons learned or advice you’d like to share, for your firm or the customer(s) involved? McLeod: Three points of advice are: • For inspecting 360-degree coverage, six readers work far better than four. • Ensure that samples of all packaging materials are provided when setting up a vision system. • Ensure that the full SKU list is provided at the start of the project to ensure that PLC memory is sized to accommodate. ce Mark T. Hoske is content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. control engineering

Food packaging processes can include accurate label inspection, although this label isn’t the application described. Courtesy: Mark T. Hoske, Control Engineering, CFE Media and Technology

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KEYWORDS: Packaging

automation, system integration case study Food packaging automation inspects package labels. Project included machine vision, code readers, PLC, software, fixturing. Automated inspection has very high rates of reliability when system are properly integrated into the application.

CONSIDER THIS Does your next machine or line redesign consider next-generation automation?

ONLINE If reading from the digital edition, click on the headline for more resources. Learn more about Grantek Systems Integration Inc. and other system integrator companies in the Global System Integrator Database. www.controleng.com/ Global-SI-Database

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ANSWERS

INSIDE MACHINES: ROBOTICS Jackie Rose, Association for Advancing Automation (A3)

New industrial mobile robot safety standard, R15.08 R15.08, American National Standard for Industrial Mobile Robots – Safety Requirements – Part 1: Requirements for the Industrial Mobile Robot.

ANSWERS KEYWORDS:

Industrial mobile robots, safety Official Standard Title is R15.08, American National Standard for Industrial Mobile Robots -- Safety Requirements -- Part 1: Requirements for the Industrial Mobile Robot Official Reference Number is ANSI/RIA R15.08-1-2020 Shorthand Name(s) are R15.08-1; IMR Safety

CONSIDER THIS R15.08-1 targets robot manufacturers; system integrators and users will be addressed in parts 2 and 3.

ONLINE This article online has more information, including “Who should use the ANS for industrial mobile robots?“ and “R15.08 Part 2 mobile robot systems, system integration, fleets; R15.08 Part 3 mobile industrial robot users.“ www.robotics.org/ robotic-standards

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n a critical first step toward common guidelines in the growing sector of mobile robotics, the new American National Standard (ANS) for safety requirements for industrial mobile robots, R15.08 Part 1, is expected to be published in November by the Robotic Industries Association (RIA), after more than four years of work and input from hundreds of industry experts. “R15.08 Part 1 will be a solid foundation for future work in this area,” said Carole Franklin, RIA director of standards development. “We focused on the industrial environment, mainly spaces where the general public do not have unrestricted access. Additional considerations will be needed for safety of the public in non-industrial environments, such as retail, and this is one example of a subject that could be covered in future work.”

What is R15.08 and how can it help mobile industrial robots?

R15.08-1 is a document providing technical requirements for the design and integration of industrial mobile robots. R15.08-1 is important because the rapidly developing capabilities of mobile robots will make industrial mobile robots (IMRs) and industrial mobile robot fleets (IMRFs) an increasingly common type of robot application in the future. For this advancement in productivity to succeed, the safety of human workers around mobile robot applications must be ensured.

Why is R15.08 needed for mobile industrial robots?

A paradigm shift has occurred in recent years with the continued advancement of mobile robots in the workplace. In the past, the American National Standard for industrial robot and robot system safety (R15.06-2012) permitted the safe use of mobile robots but did not provide detail on how to do this. R15.06 focuses on manipulator-type industrial robots (“arms”).

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Other related existing standards included AGV safety standards, such as B56.5 for Driverless, Automatic Guided Industrial Trucks (automatic guided vehicles or “AGVs”) and ISO 3691-4 Industrial trucks – Safety requirements and verification – Part 4: Driverless industrial trucks and their systems. These documents have historically been written with the assumption the AGV relied on some form of predetermined guidepath rather than autonomous navigation. “AMRs and mobile manipulators need safety requirements beyond what existed in R15.06, which generally assumed industrial robots were stationary, or B56.5, which historically assumed that mobile machines needed predetermined guidepaths,” Franklin said.

R15.08 includes use of an industrial robot arm on a mobile robot

Until now, mobile robot manufacturers had only general safety requirements for industrial machinery. R15.08 Part 1 provides a common set of requirements for industrial mobile robots and yet is flexible enough to permit companies to develop their own solutions. A particular missing piece from R15.06 and B56.5 was how to combine an industrial manipulator with a mobile platform, especially when each had its own safety controller. For example, for a “mobile manipulator” type of robot, the question could be asked of what to do in the case when a manipulator is mounted to an AGV as a mobile platform? In that case, which safety standard takes precedence, R15.06 for the manipulator (“industrial robot” per R15.06) or B56.5 (for the AGV as the mobile platform)? R15.08 Part 1 provides such detailed requirements. ce

Jackie Rose is marketing and communications manager Association for Advancing Automation (A3), a CFE Media content partner. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. www.controleng.com

PLC + HMI

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ANSWERS

INSIDE MACHINES: MOBILE ROBOTICS Matthew Wade, BlueBotics

Five tips for specifying mobile robots Before investing in an industrial mobile robot, get answers for these five questions.

f you are thinking of moving to a mobile robot or automated guided vehicle (AGV), there are questions that should be asked. BlueBotics chief executive officer, Nicola Tomatis, with a PhD in robotics, covered five key areas to consider before buying mobile robotics.

1. How is the vehicle installed?

Tomatis: You may have asked lots of questions about a vehicle’s capability, but have you considered how it will be installed? Will it take a matter of hours, a day or so, or weeks of integration requiring third-party personnel on site and disrupting normal operations?

2. How easy is it to adapt and change routes?

M More ANSWERS

KEYWORDS: Mobile robots,

mobile robotics, robot specifications Mobile robotic installation should be disruptive when integrating with existing systems. Changing paths for mobile robots shouldn’t be difficult. Robot maintenance should be part of the plan when investing in a fleet.

CONSIDER THIS Visit an existing mobile robotic operator to get feedback before you decide.

ONLINE See an image for a mobile disinfectant robot. www.controleng.com/robotics

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Tomatis: All businesses change and grow, meaning you are likely to want to change the routes on which the robot is operating. Is this a simple change to “digital” paths, or does it require more substantial, physical changes? Make sure your investment is not going to be a huge ongoing overhead every time your business takes a new direction.

3. Can you scale your vehicle fleet?

Tomatis: You may not need multiple vehicles today, but you should consider what happens when you do. Adding a new mobile robot or AGV should not be a whole new project. Look at what fleet management options are offered with the vehicle and how easy it is to add new vehicles over time.

control engineering

The mini UVC, jointly developed by BlueBotics and Engmotion, is mobile robot that disinfects as programmed every using UVC lamp technologies. It can be used alone or in a connected fleet to autonomously disinfect hospitals, airports, hotels, and commercial/industrial sites. BlueBotics uses its autonomous navigation technology (ANT) mobile robot platform to simplify and shorten installation of AGVs and mobile robots. Courtesy: BlueBotics SA

It is also worth asking whether you are tied to one type or brand of robot or vehicle, or whether the system can accommodate others, so you are not locked into one vendor.

4. What kind of maintenance plan is offered?

Tomatis: All machines need maintenance and your vehicles are only useful when they are working reliably – so, keep them working. Ensure you have access to a maintenance plan that suits your business’ needs.

5. How proven is the system in real-world applications?

Tomatis: Someone must go first, but it does not have to be you – and, if it is, you do not want that to be a surprise. Be clear whether you are one of the first customers, or whether thousands of vehicles are installed and proven in global applications so you can understand the risks associated with your choice. ce

Matthew Wade is head of marketing, BlueBotics SA. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com. www.controleng.com

ANSWERS

INSIDE MACHINES AND ROBOTICS Christian Fell, Posital-Fraba Inc.

How hollow-shaft encoders break the multi-turn barrier Adding multi-turn measurement capabilities to hollow-shaft encoders is easier and expands applications (including robotics) with a Wiegand wire system.

ing-shaped (hollow-shaft) encoders can be an attractive alternative for motion control applications where their open center form-factor gives engineers extra flexibility when laying out their designs. In the past though, most encoders of this type have been limited to a single turn measurement range. A new generation of hollow-shaft encoders has overcome this limitation thanks to a zero-maintenance Wiegand-powered multi-turn rotation counting system. Hollow-shaft encoders fill an important niche in motion control, providing designers of servomotors, drives and robots with options for building position feedback sensors into products. This encoder type has a large central opening, which makes it convenient to install them on the driveshaft end of motors or gear units. Hollow-shaft encoders are well suited to robots and can be built into joints to measure arm positions directly. An open-center form factor leaves room for structural elements or to route wires or pneumatic/hydraulic lines through the center of the joint. Adding multi-turn measurement capabilities to hollow-shaft encoders helps many applications, such as when the motor is connected to a reduction gear system or cable spool. For robots, a multi-turn measurement range is ideal for joints with a range of travel of over 360 degrees. Until recently, adding multi-turn capabilities to hollow-shaft encoder has been challenging due to the open center shape. This difficulty has been overcome by adapting a Wiegand wire system, which uses the rotation of a set of magnets to trigger a rotation counter system built into the encoders. An advantage of this approach is the counter system is largely self-powered, eliminating the need for troublesome backup batteries or bulky, complex gear systems.

www.controleng.com

Figure 1: Ring-shaped (hollow-shaft) encoders can be an attractive alternative for motion control applications; open center form-factor gives engineers extra flexibility when laying out designs. Images courtesy: Posital Fraba

Hollow-shaft encoders with large center openings are often based on capacitive measurement technologies since – unlike magnetic technologies – the design does not require location on the center line of the instrument. Capacitive encoders have rotor and stator components shaped like flattened rings. Components’ facing surfaces have patterned conductive surfaces that function as capacitor plates. As the rotor turns, these conductive patterns change relative positions, altering the capacitive coupling across the system. This modulates the amplitude and phase angle of mid-frequency electrical signals transmitted through the capacitor system. Signal variations are decoded to determine the rotor’s angular position. Accuracy is very high, with a 19-bit resolution. Since capacitance is averaged around the full circumference of the ring-shaped KEYWORDS: Encoders, motion rotor and stator components, the system control is relatively insensitive to small alignment Hollow-shaft encoders are used for many motion control applications, but errors or the presence of moisture or dust.

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Adding multi-turn capability

The key to the multi-turn measurement range is a counter system powered by energy harvested from the motion of the rotor element. While Wiegand energy harvesting systems have often made use of a permanent magnet mounted on the centerline of a drive shaft, a completely new setup had to be found for the hollow-shaft design. Extensive field tests and magnetic field simulations resulted in an arrangement of four permanent

are often limited by how many turns they can perform. The Wiegand effect allows hollowshaft encoders to make multiple turns, which increase the number of applications they can be used for.

ONLINE Learn more about encoders and other discrete manufacturing topics at www.controleng.com.

CONSIDER THIS What applications would benefit most from hollow-shaft encoders in your plant?

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ANSWERS

INSIDE MACHINES AND ROBOTICS Many PLCs and microcontrollers support the interface. It requires no licensing arrangements.

The Wiegand effect

Figure 2: Exploded view of a capacitor encoder shows components.

magnets mounted on the circumference of the rotor. Magnets create a stable magnetic field that turns with the rotor. As the rotor turns, a Wiegand sensor mounted on the stator responds to changing magnetic field and generates pulses of electric current that activate the counting electronics, recording each revolution.

Energy harvesting refers to techniques for collecting electrical energy directly from the local environments, reducing the need for backup batteries and their attendant maintenance requirements. While piezo systems, thermal and kinetic processes have set the pace for energy harvesting, the Wiegand effect, named for the U.S. inventor John Wiegand, is still considered an exotic alternative. The core of the Wiegand system is a short length of specially-conditioned Vicalloy wire. At the end of a complex manufacturing process, involving cold forming and tempering, the wire emerges with a soft magnetic core surrounded by a magnetically hard layer. This combination has a special feature. When the Wiegand wire is exposed to a changing external magnetic field, the magnetic polarity of the wire will reverse when

the intensity of the magnetic field around the wire reaches a certain threshold. This sudden change of magnetic polarity can be harnessed to generate a pulse of electric current in a fine copper coil wrapped around the wire. The technology has been used for energy harvesting since 2005, and an important factor for this breakthrough in energy harvesting was the availability of ultra-efficient low-power electronic chips. Energy harvesting using the Wiegand effect is magnetically induced directly from the rotary motion. Unlike a dynamo, energy is produced with each turn is consistent – even for near-zero rotation speeds. A Wiegand sensor is 15 mm long and yield almost 200 nJ of energy at 7 V – enough to reliably activate the rotation counters and associated electronics. ce Christian Fell is director of technology development, Posital-Fraba Inc. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

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input #14 at www.controleng.com/information

Make your motion easier. Visit aerotech.com/automation1.

AT0520A-CSG

INNOVATIONS

ENGINEERS’ CHOICE AWARDS Amanda Pelliccione, Research Director

Vote now for Engineers’ Choice Finalists The official ballot is open for voting for Control Engineering North American print and digital edition subscribers, for a limited time. Cast your vote using CFE Media’s New Products for Engineers platform at www.controleng.com/NPE.

ote now! For a limited time, the official Engineers’ Choice ballot is open for voting for Control Engineering North American print and digital edition subscribers. At www.controleng.com/NPE, vote for the best Engineers’ Choice finalists of 88 entries across 21 categories. Based on your experience, please vote in as many categories for which you feel qualified based on technological advancement, service to the industry, and market impact. Details and photos are available for each product. Winners and honorable mentions will be featured in more detail in the February 2021 issue of Control Engineering.

Informed voting is an important responsibility. Voting on this ballot is only open to qualified* subscribers of Control Engineering products. One ballot per qualified subscriber will be accepted; multiple ballots from the same qualified subscriber will be invalid. Ballots received from non-qualified subscribers will be invalid. (*Employees of product manufacturers with a finalist in the current program and their properties, agencies, vendors, and representatives—even if Control Engineering subscribers—are ineligible to vote.) Amanda Pelliccione is CFE Media’s research director and manager of awards programs, apelliccione@cfemedia.com.

• AMAX-5580 embedded controller, Advantech, www.advantech.com

• IMScompact integrated measuring system, Bosch Rexroth, www.boschrexroth-us.com

• NetWall unidirectional security gateway, Bayshore Networks, www.bayshorenetworks.com

• OSA Remote +Flow industrial control system, Bedrock Automation, https://bedrockautomation.com

• GM-1000 rugged GPU compacting platform, Cincoze Co., www.cincoze.com

• Otfuse industrial security appliance, Bayshore Networks, www.bayshorenetworks.com

• CP2E programmable logic controller, Omron Automation Americas, https://automation.omron.com

• In-Sight D900 smart camera, Cognex Corp., https://cognex.com

• GPAidentify V2 asset monitoring and cybersecurity software, GPA, www.global-business.net

• I/O System Advanced automation system for mechanical engineering, Wago Corp., www.wago.com/us

• MicroHawk V430-F industrial Ethernet barcode reader, Omron Automation Americas, https://automation.omron.com

Cybersecurity

• Engineer-in-a-Box remote access device, Grantek, https://grantek.com Hardware – HMI, Operator Interface, Thin-Client • HMIS Multi-Legend Alarm Indicator, EAO, www.eao.com • Simatic MTP1500 HMI Unified Comfort Panel, Siemens, www.usa.siemens.com Hardware – Industrial PCs, CNCs • C6025 ultra-compact industrial PC, Beckhoff Automation, www.beckhoffautomation.com • Nuvo-7531 compact fanless computer, Neousys Technology America, www.neousys-tech.com • Simatic Field PG M6 industrial computer, Siemens, www.usa.siemens.com • Sinumerik One digital native CNC, Siemens Industry, https://usa.siemens.com • ztC Edge 100i/110i edge computing platform, Stratus Technologies, www.stratus.com Industrial Internet of Things Connectivity – Edge Controller

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• Vicore dual camera vision system, Teledyne Dalsa, www.teledynedalsa.com

Industrial Internet of Things Connectivity – Software • Matrikon Dispatch Data Broker, Honeywell Process Solutions, www.honeywellprocess.com/en-us • ThingWorx Kepware Server industrial connectivity software, PTC Kepware, https://ptc.com

• ActiNav autonomous bin picking kit for machine tending, Universal Robots, www.universal-robots.com Motion Control • EP7402 EtherCAT Box for conveyor control, Beckhoff Automation, www.beckhoffautomation.com

• FactoryTalk Linx Gateway V6.2 control design software, Rockwell Automation, www.rockwellautomation.com

• Allen-Bradley Compact GuardLogix 5380 SIL 3 controller, Rockwell Automation, www.rockwellautomation.com

• TIA Portal V16 Step 7 with Version Control Interface, Siemens, www.usa.siemens.com

• iTrak 5730 small frame linear motor, Rockwell Automation, www.rockwellautomation.com

Machine & Embedded Control – PLCs • ProductivityOpen Arduino-compatible controller, AutomationDirect, www.automationdirect.com

Motion Control – Drives • Simplified Motion Series electric drive, Festo, www.festo.com/us

• UniStream 10” Built-in multi-function PLC, Unitronics, www.unitronicsplc.com

• Allen-Bradley PowerFlex 6000T medium voltage drive, Rockwell Automation, www.rockwellautomation.com

Machine Vision, Code Readers, Discrete Sensors • Programmable Smart Encoder, Autotech Controls, www.autotechcontrols.net

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INNOVATIONS

ENGINE ERS’ CHOICE AWARDS Motion Control – Drives, Servo

Power – Energy, Power Protection

Cast your vote at www.controleng.com/NPE.

Safety – Machine Safety

• ctrlX Drive compact modular drive system, Bosch Rexroth, www.boschrexroth-us.com

• SCT current transformer, Beckhoff Automation, www.beckhoffautomation.com

• F3SG-PG safety light curtain, Omron Automation Americas, https://automation.omron.com

• Motion Made Easy PowerTools integration module (PTi210), Control Techniques, www.controltechniques.us

• MiniRaQ vertical wall-mount server rack enclosure, Eaton, www.eaton.com

• Safety Light Curtain with Bluetooth interface, Schmersal, www.schmersalusa.com

• Pow-R-Line Xpert intelligent panelboard, switchboard, Eaton, www.eaton.com

• Simatic ET 200eco PN F-DI 8 x 24 VDC fail-safe module, Siemens, www.usa.siemens.com

• Kinetix 5300 servo drive, Rockwell Automation, www.rockwellautomation.com • Simatic Drive Controller, Siemens, www.usa.siemens.com • Simatic Micro-Drive extra-low voltage servo drive, Siemens Industry, https://usa.siemens.com/pi Network Integration – Ethernet Hardware, Switches • Digi IX20 cellular router, Digi International, www.digi.com • T1 Industrial connector, Harting NA, http://harting-usa.com • FL Switch 1000 unmanaged Ethernet switch, Phoenix Contact, www.phoenixcontact.com/us • Allen-Bradley ControlLogix EtherNet/IP communication module, Rockwell Automation, www.rockwellautomation.com • ET 200SP MultiFieldbus Interface Module IM1556MF HF, Siemens, www.usa.siemens.com Network Integration – I/O Systems • groov RIO intelligent, Ethernet-based I/O unit, Opto 22, www.opto22.com • Allen-Bradley 1718 Ex I/O module, Rockwell Automation, www.rockwellautomation.com • Allen-Bradley Flex 5000 I/O R2 integrated HART I/O module, Rockwell Automation, www.rockwellautomation.com • Active backplane bus for Simatic S7-1500 I/O modules, Siemens, www.usa.siemens.com • Simatic IOT 2050 gateway, Siemens, www.usa.siemens.com • I/O System Field, Wago Corp., www.wago.com/us

• EMpro energy monitoring device, Phoenix Contact, www.phoenixcontact.com/us • PowerLogic ION9000 power quality meter, Schneider Electric, www.schneider-electric.com • Tobjob S function terminal block, Wago Corp., www.wago.com/us • Omnimate Power Bus connection system, Weidmuller, www.weidmuller.com Power Supply, UPS • 960 Gen4 selectable voltage power supply, Exair, www.exair.com • Pro 2 power supply, Wago Corp., www.wago.com/us Process Control – Process Sensors, Transmitters • Liquiphant FTL51B point level switch, Endress+Hauser, http://us.endress.com • P9098 pressure sensing digital flowmeter, Exair, www.exair.com • FS-DSL Digital Sensor Link for Sitrans FST030, Siemens Industry, https://usa.siemens.com/pi • Sitrans LR100 compact radar transmitter, Siemens Industry, https://usa.siemens.com/pi Process Control Systems • Experion PKS Highly Integrated Virtual Environment, Honeywell Process Solutions, www.honeywellprocess.com/en-us • Plant PAx 5.0 distributed control system, Rockwell Automation, www.rockwellautomation.com

How to Cast Your Vote VOTING FOR the Control Engineering 2021 Engineers’ Choice Awards program opens Oct. 8, 2020, and is hosted within the New Products for Engineers platform, www.controleng.com/NP4E. Voting is only open to qualified subscribers of Control Engineering products (magazine—print or digital, enewsletters, white papers, etc.). Qualified subscribers are encouraged to vote in as many categories for which they are qualified based on technological advancement, service to the industry and market impact. Read more about voting eligibility via the program’s Official Rules, www.controleng.com/EngineersChoice. 1. Qualified voters, please register for a new user account or sign in to your existing user account within New Products for Engineers: www.controleng.com/NP4E 2. Select “Awards” from the menu bar to arrive at the “Award Programs” page. Select “Control Control Engineering 2021 Engineers’ Choice Awards” to view the finalists in their categories and cast your votes. 3. Review, submit and/or edit your votes by Friday, Dec. 31, 2020.

Software – Asset Management, Reporting • Enabled Services control system health and performance software, Honeywell Process Solutions, www.honeywellprocess.com/en-us • Entis Inventory System R120, Honeywell Process Solutions, www.honeywellprocess.com/en-us • Hyper Historian V10.96 data historian software, Iconics, https://iconics.com • FactoryTalk AssetCentre V10 asset management software, Rockwell Automation, www.rockwellautomation.com • Sinec Infrastructure Network Services software, Siemens, www.usa.siemens.com • TIA Selection Tool V2020.8 device configuration software, Siemens, www.usa.siemens.com Software – Control Design • Automation1 MDK motion control platform, Aerotech, www.aerotech.com • Movicon.NExT 4.0 industrial platform, Progea North America Corp., www.progea.com • ControlFlash Plus V3 firmware management software, Rockwell Automation, www.rockwellautomation.com • Emulate3D digital twin software, Rockwell Automation, www.rockwellautomation.com • Studio 5000 V33 design environment, Rockwell Automation, www.rockwellautomation.com • TIA Portal Test Suite Advanced V16 programming software, Siemens, www.usa.siemens.com Software – Data Analytics • EnergyPQA.com cloud-based energy management system, Electro Industries/GaugeTech, www.electroind.com • Movicon Pro.Lean 4.0 analytics software, Progea North America Corp., www.progea.com • Seeq R22 advanced analytics for process manufacturing, Seeq Corp., www.seeq.com • Performance 360 process performance, optimization software, Symphony AzimaAI, https://symphonyazimaai.com Software – HMI Software • Augmented reality for Plantweb Optics asset performance platform, Emerson Automation, www.emerson.com • Movicon WebHMI 4.0 HMI visualization software, Progea North America Corp., www.progea.com • FactoryTalk View V12 HMI software, Rockwell Automation, www.rockwellautomation.com • Simatic WinCC Unified V16 visualization software, Siemens, www.usa.siemens.com • VTScada V12 integrated HMI, SCADA platform, Trihedral, www.vtscada.com

READ MORE about the 88 finalists for 2021, see images, and, if eligible, cast your vote responsibly at www.controleng.com/NPE.

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Building The IoT From Edge To Cloud Josh Eastburn | Director of Technical Marketing / Opto 22

Sharing process data has long been a goal of industrial automation, but traditional operational technologies (OT) are poor at scaling, priced prohibitively, and demand complex configuration and support. End users would like to leverage the industrial internet of things (IIoT) to share data seamlessly across machines, sites, and the enterprise to help them optimize production and discover new cost-saving opportunities. The traditional, multi-layer approach was necessary when computing capability, network bandwidth, and security features were much less available. Each step up the hierarchy, from a basic hardwired sensor to cloud computing systems, was required to access greater computing and networking resources.

Today, the relationship is changing because sensors and other edge devices are far more capable, with some of them including processing, communications, and security abilities similar to a PC. Edge I/O and edge controllers provide TLS (transport layer security) encryption, VPN (virtual private networking) for secure remote connection, and DHCP (dynamic host configuration protocol) for automatic addressing.

Rather than requiring layers of supporting middleware, they provide their own connectivity tools, including MQTT/ Sparkplug B, OPC UA, and IoT platforms like Node-RED, making these devices first-class participants in distributed systems. With edge devices making data available to OT and IT systems at the edge and up to higher organizational levels, the logical hierarchy can be flattened even as the geographical distribution is expanded. Data acquisition and distribution can happen more flexibly, scalably, and affordably, which creates new architectural options, including: • Shared site-to-site infrastructure using edge data processing • IoT integration of legacy devices and systems • Direct-to-cloud I/O publishing networks • Many-to-many MQTT infrastructure This white paper explores the complexities of traditional architectures, looks at specific features of industrial edge devices designed to simplify integration, and discusses how a more distributed architecture enables connectivity from the field to the cloud for industrial devices and the I/O systems and controllers linked to them. Download this white paper at: http://op22.co/EdgeCloudWhitePaper

info@opto22.com www.opto22.com

input #15 at www.controleng.com/information

INNOVATIONS

See more New Products for Engineers. www.controleng.com/NPE

NEW PRODUCTS FOR ENGINEERS

Extended protection safety light curtains eliminate blind spots AutomationDirect has added Contrinex slim-profile safety light curtains, used for human protection and product/machine safety, to its extensive safety products lineup. The new 14mm and 30mm beam resolution extended-protection, slim-profile safety light curtains eliminate blind spots (the height of the light curtain is the height of the protected area) and the slim low profile makes safety curtain installation inside the machine application easier. These new slim light curtains can also be wirelessly configured through Bluetooth. The 14mm resolution light curtains provide finger safe protection and 30mm resolution curtains offer hand safe protection. AutomationDirect, www.automationdirect.com

Input #200 at www.controleng.com/information

I/O system field

Temperature transmitter and signal converter

Moore Industries’ TMZ PC-programmable Modbus temperature transmitter and signal converter dualuniversal input model accepts two inputs that can be configured for current and voltage, or for RTD, thermocouple, mV, potentiometer and resistance. The device converts the inputs to the standard Modbus RTU communication protocol ready for direct interface with Modbus-based monitoring and control systems. Up to 32 TMZs (without repeaters) can be multidropped onto one low-cost communication link (such as a twisted wire pair). Moore Industries, www.miinet.com

Input #201 at www.controleng.com/information

Statement of Ownership, Management and Circulation 1. 2. 3. 4. 5. 6. 7.

Publication Title: CONTROL ENGINEERING Publication Number: 813-430 Filing Date: 9/25/20 Issue Frequency: 12x, monthly Number of Issues Published Annually: 12 Annual Subscription Price: USA $165 CAN $200 MEX $200 INTL $350 Complete Mailing Address of Known Office of Publication (Not printer): CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 8. Complete Mailing Address of Headquarters or General Business Office of Publisher (Not printer): CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 9. Publisher: Jim Langhenry, CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 Editor-in-Chief: Mark Hoske, CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 Editor: Chris Vavra, CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 10. Owner: CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 Jim Langhenry and Steve Rourke, CFE MEDIA, LLC 3010 Highland Parkway, Ste #325, Downers Grove, IL, 60515 11. Known Bondholders, Mortgagees, and Other Security Holders Owning or Holding 1 Percent or More of Total Amount of Bonds, Mortgages, or Other Securities: None 12. Does not Apply 13. Publication Title: Control Engineering 14. Issue Date for Circulation Data Below: September 2020 15. Extent and Nature or Circulation Average No. Copies Each Issue During Actual No. Copies of Single Issue Preceding 12 Months: Published Nearest to Filing Date: a. Total Number of Copies (Net Press Run): 27,185 34,363 b. Paid and/or Requested Circulation: 00 00 (1) Paid/Requested Outside-County Mail Subscriptions Stated on Form 3541. 26,781 34,050 (Include advertiser’s proof and exchange copies) (2) Paid In-County Subscriptions Stated on Form 3541. (Include advertiser’s proof and exchange copies) (3) Sales Through Dealers and Carriers, Street Vendors, Counter Sales, and Other Non-USPS Paid Distribution (4) Paid Distribution by Other Classes of Mail Through the USPS c. Total Paid and/ or Requested Circulation [Sum of 15b, (1), (2), (3), and (4)-** d. Free or Nominal Rate Distribution (By Mail and Outside the Mail) (1) Outside-County as Stated on Form 3541 (2) Free or Nominal Rate In-County Copies Included on PS Form 3541 (3) Free or Nominal Rate Copies Mailed at Other Classes Through the USPS (4) Free or Nominal Rate Distribution Outside the Mail (Carriers or other means) e. Total Nonrequested Distribution [Sum of 15d (1), (2), (3), and (4) f. Total Distribution [Sum of 15c and 15f] g. Copies not Distributed h. Total [Sum of 15f and 15g] i. Percent Paid [15c divided by 15f times 100]

51 26,832 0 0 0 221 0 221 27,054 131 27,185 99.18%

46 34,096 0 0 0 163 0 163 34,259 104 34,363 99.52%

16. Electronic Copy Circulation 49,154 47,798 a. Requested and Paid Electronic Copies b. Total Requested and Paid Print Copies (Line 15c) + 75,935 81,894 Requested/Paid Electronic Copies (Line 16a) c. Total Requested Copy Distribution (Line 15f) + 76,156 82,057 Requested/Paid Electronic Copies (16a) d. Percent Paid and/or Requested Circulation (Both Print & Electronic Copies) 99.71% 99.80% (16b divided by 16c x 100) 17. Publication of Statement of Ownership: Publication Required. Will be printed in the October 2020 issue of this publication. 18. I certify that all information furnished on this form is true and complete. I understand that anoyone who furnishes false or misleading information on this form or who omits material or information requested on the form may be subject to criminal sanctions (including fines and imprisonment) and/or civil sanctions (including civil penalities). Paul Brouch (signed), Director of Operations

Wago’s I/O System Field is designed to meet the requirements of modern decentralized production facilities providing maximum performance and high level connectivity. The system is IP67 rated and offers non-molded plastic housings with low mass for mobile applications such as robots. Future releases will support EtherNet/IP and EtherCAT protocols. These modules are designed for the time-sensitive networking (TSN) standard, support OPC UA communications and can be configured via smart device app using Bluetooth wireless. Wago Corp., www.wago.com Input #202 at www.controleng.com/information

Module for monitoring efficiency values

Progea’s Pro.Lean is a functional module that allows you to measure the overall efficiency values in real time by collecting and aggregating production process data from different sources at production level (PLC, HMI, SCADA). This data can then be analyzed to assess the real time plant situation by showing the overall production indicators independently from the deriving data source, with the aim to reduce loss and maximize profits. Progea North America Corp., www.progea.com Input #203 at www.controleng.com/information

INNOVATIONS

BACK TO BASICS: INDUSTRIAL INTERNET OF THINGS Industrial Internet Consortium (IIC)

Industrial Internet of Things vocabulary terms updated The Industrial Internet Consortium (IIC) released updated definitions for terms such as the Internet of Things, cloud computing and more.

he Industrial Internet Consortium’s (IIC) Industrial Internet Vocabulary Technical Report version 2.3 is one of six IIC technical reports that provides industry guidelines on vocabulary, architectures, security, analytics, connectivity and business strategy.

Internet of Things (IoT) terms

Version 2.3 of the report includes new or updated definitions that enhance Internet of Things (IoT) vocabulary terms, including: • Internet of Things (IoT): A concept where components are connected via a computer network, and where one or more of those components interacts with the physical world. • IoT system: A system where the components are connected via a computer network, and one or more of those components interacts with the physical world. • Industrial IoT (IIoT) system: An IoT system used in an industrial context. “The Internet of Things is evolving, and our understanding of IoT concepts is evolving as well,” said Erin Bournival, co-chair of the IIC Vocabulary Task Group and distinguished engineer, office of the corporate CTO at Dell Technologies in a press release. “We’re excited to provide new definitions for these fundamental terms to increase clarity for all IoT stakeholders.”

IIC computer terms

Other terms with expanded definitions focus on computer networking. These include: • Cloud computing: Paradigm for enabling computer network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand. • Connectivity: Ability of a system or application to communicate with other systems or applications via computer network(s). • Endpoint: Component that has computational capabilities and computer network connectivity.

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• Event: Any observable occurrence in a system and/or computer network.

Edge computing, IS, IT, OT

Other definitions from the 158 definitions in V2.3 of the report include: • Edge computing: Distributed computing that is performed near the edge, where the nearness is determined by the system requirements. • Industrial control system (ICS): Combination of control components that act together to exercise control in the physical world. • Information technology (IT): Entire spectrum of technologies for information processing, including software, hardware, communications technologies and related services. • Operational technology (OT): hardware and software that detects or causes a change through the direct monitoring and/or control of physical devices, processes and events in the enterprise. • Security: Property of being protected from unintended or unauthorized KEYWORDS: Industrial internet, IIoT, access, change or destruction ensuring Internet of Things availability, integrity and confidentiality. The Industrial Internet Consortium • Situational awareness: Within a (IIC) released an updated report on volume of time and space, the percepdefinitions related to Industrial Internet tion of an enterprise’s security posture of Things (IIoT) vocabulary. and its threat environment; the compreA total of 158 words and phrases were defined. hension/meaning of both taken togethThe report also included four terms er (risk); and the projection of their that are being discouraged from the status into the near future. future use. The report includes four terms that ONLINE are discouraged as ambiguous or conRead additional stories from the IIC flicting with accepted interpretations. at www.controleng.com. Read this online (click the headline, if 10 industrial Internet terms you you’re reading the digital edition) for should know those four terms. ce

M More INNOVATIONS

- Edited from an Industrial Internet Consortium (IIC) press release by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com. The IIC is a CFE Media content partner.

Industrial Internet Consortium Updates IoT Vocabulary Terms Consortium updates Industrial Internet Vocabulary Technical Report

CONSIDER THIS What definitions not featured here or in the report do you think need to be highlighted?

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ANSWERS

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Continued from p. 32

Address AI bottlenecks

contributors to inefficiency that arise from frequent access to the main memory for handling intermediate input and output and the interface between local and main memories. In the main memory, data is stored digitally, but it must be converted to analog when it is brought into the local memory for processing in-memory. In prior ReRAM PIM accelerators, the resulting values are converted from analog to digital and sent back to the main memory. If they are called from the main memory to local ReRAM for subsequent operations, they are converted to analog yet again, and so on. TIMELY avoids paying overhead for both unnecessary accesses to the main memory and interfacing data conversions by using analog-format buffers within the local memory. In this way, TIMELY mostly keeps the required data within local memory arrays, greatly enhancing efficiency. The group’s second proposal at ISCA 2020 was for SmartExchange, a design that marries algorithmic and accelerator hardware innovations to save energy. “It can cost about 200 times more energy to access the main memory – the DRAM – than to perform a computation, so the key idea for Smar-

tExchange is enforcing structures within the algorithm that allow us to trade higher-cost memory for much-lower-cost computation,” Lin said. “For example, let’s say our algorithm has 1,000 parameters,” she added. “In a conventional approach, we will store all the 1,000 in DRAM and access as needed for computation. With SmartExchange, we search to find some structure within this 1,000. We then need to only store 10, because if we know the relationship between these 10 and the remaining 990, we can compute any of the 990 rather than calling them up from DRAM. “We call these 10 the ‘basis’ subset, and the idea is to store these locally, close to the processor to avoid or aggressively reduce having to pay costs for accessing DRAM,” she said. The researchers used the SmartExchange algorithm and the custom hardware accelerator to experiment on seven benchmark deep neural network models and three benchmark datasets. They found the combination reduced latency by as much as 19 times compared to state-of-the-art deep neural network accelerators. ce

‘

It can cost 200 times more energy to access main memory than to perform a

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