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Control, Instrumentation and Automation in the Process and Manufacturing Industries November 2020

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More control for OT and IT

Turning data into actionable information

Are cobots finding their place?

Documenting the rise of the digital twin

| PC11-54E |

The C7015: bringing multi-core in IP 65/67 directly to the machine

www.beckhoff.com/c7015 Up to four cores in IP 65/67: with its extremely robust, fanless C7015 ultra-compact Industrial PC, Beckhoff as a specialist in PC-based control technology offers the possibility to install a high-performance Industrial PC in a highly compact design directly at the machine. Versatile on-board interfaces enable connection to the cloud or to other networks. The integrated Intel Atom® CPU with up to four cores allows simultaneous automation, visualization, and communication in demanding industrial IP 65/67 applications. In addition to classic control tasks, the C7015 is ideally suited for use as a gateway to connect machines and plant sections – and can even handle complex preprocessing of large data volumes thanks to its high processing power.

connect

The digital automation hub

Connect with the Beckhoff experts: www.beckhoff.com/sps 3 x LAN, 2 x USB, Mini DisplayPort and integrated EtherCAT P port

CONTENTS Looking forward to 2021

Editor Suzanne Gill suzanne.gill@imlgroup.co.uk Sales Manager Adam Yates adam.yates@imlgroup.co.uk Group Publisher Iain McLean iain.mclean@imlgroup.co.uk Production Holly Reed holly.reed@imlgroup.co.uk Dan Jago David May G and C Media

Group Publisher Production Manager Studio Design

Well, this is the last issue of Control Engineering Europe for 2020, and what a damp squib of a year it has turned out to be! Usually I would now be frantically organising my diary to try and renew as many acquaintances as possible at the annual SPS – smart production solutions show. Instead, I am consigned to my desk at home, talking to people via one of the many video conferencing platforms that, until earlier this year, I had never heard of.

The pandemic has certainly demanded a rapid change in the way we all work and thankfully we do have digital technology to help keep our plants running and allowing us all to communicate. For anyone who has resisted the move to digitalisation, 2020 will hopefully have opened their eyes. For business success in the future, digitalisation is no longer optional!

I wish you all a peaceful and healthy end to what has been an awful year, and hopefully we can emerge from our home offices again some time in 2021 and return to some kind of normal.

Suzanne Gill Editor – Control Engineering Europe suzanne.gill@imlgroup.co.uk

INDUSTRY REPORT

DIGITAL TWINS

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20 Process simulation – what can a digital twin do?

Live video streaming of inspection and repair offshore reduces personnel numbers

22 Find out how and why the digital twin is set to be a key technology for the success of Industry 4.0

EDITOR’S CHOICE 6

Rugged edge computers for AIoT computing; AI solutions accelerate the journey to the selfoptimising plant

PROCESS CONTROL 24 Making process control more flexible

DATA ANALYTICS

SKILLS

10 Turning data into actionable information is vital to the success of any Industry 4.0 project.

Suzanne Gill reports

26 Do machine manufacturers need more knowledge about artificial intelligence (AI) for machine learning (ML) applications?

14 Following the data science for greater production efficiency

ROBOTICS 16 We report on the benefits of an open source language for robots 17 Suzanne Gill finds out where cobots can offer the most value in today’s manufacturing processes

Control Engineering Europe is a controlled circulation journal published eight times per year by IML Group plc under license from CFE Media LLC. Copyright in the contents of Control Engineering Europe is the property of the publisher.

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November 2020

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INDUSTRY REPORTS

Live video streaming of inspection and repair offshore reduces personnel numbers ROVOP has delivered its first remote platform-based inspection, repair and maintenance workscope, which can help reduce the number of personnel required offshore.

The provider of subsea remotely operated vehicles (ROVs) carried out remote visual and NDT inspections of hull sections, flowlines, umbilicals and risers, along with chain inspection, measurement and cleaning on the Balmoral floating production vessel for Premier Oil. Using communications and modelling technology, ROVOP worked with Premier Oil to develop a robust live video streaming service back to shore. Two-way open communications allowed the inspection and data recording engineers to run the workscope remotely from onshore, resulting in three less people needed on board the vessel.

The cloud-based viewing platform allowed those working from home to view the inspection work as it unfolded. They were able to see exactly what the ROV and inspection engineers were seeing in real-time. Data, which would once have taken weeks to return

from offshore to be analysed, was captured and those watching onshore were able to influence the operation live, making the campaign more efficient.

Paul Hudson, ROVOP’s sales and marketing director, said: “At ROVOP we are always looking to push the operational envelope by deploying the latest technology and the best people to solve problems and deliver results.

Reducing numbers of people offshore has clear benefits in terms of risk, cost and overall efficiency and, of course, it is particularly relevant when dealing with the challenges presented to the offshore industry by the coronavirus pandemic. This project underlines how digitalisation and collaboration can address some of our most pressing industry challenges.”

David Robertson, diving & ROV

engineer with Premier Oil, added: “Through a lot of hard work and collaboration with respective network technology companies, we managed to de-risk personnel travelling to an offshore installation during the pandemic. Executing work of this nature from an installation is always challenging due to bed space requirements. We have proven that inspection activities can be done with a significant reduction in manpower offshore, which potentially paves the way for cost and greenhouse gas reductions across our other assets in the future”.

Industrial sensor market set to grow

According to a recent study from Global Market Insights, growth in industrial sensor market is stemming from rapid proliferation of IoT in various industrial applications, along with growing demand for automation in the manufacturing sector.

Collection of real-time data of various operations and increases in production capability are some of the key advantages acquired through the deployment of industrial sensors in the manufacturing sector.

Integration of cloud computing technologies with industrial sensors is bringing enhanced results in manufacturing facilities, which will favour product adoption over the forecast timeframe. With the advent of industry 4.0, manufacturers are progressively adopting automated solutions to improve their operational capabilities. This is likely to fuel the adoption of industrial sensors across various industry verticals for analysing data and eliminating the need for human intervention.

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Industrial sensors are already being accepted across the energy and power sector owing to product advantages such as increased reliability, robustness, and durability of operations. In 2019, the energy & power application accounted for a market share of around 7% and is expected to showcase a growth rate of 7% over the analysis period.

This can be attributed to the growing adoption of IoT technology in the energy & power sector. Rapid integration of new technology into sensor modules has aided analysis and transfer of data in real time for several parameters, including temperature, pressure, and flow rate.

Adoption of automation technologies across numerous industries has fuelled the demand for level sensors for control and safety applications with these sensors increasingly being used for leakage detection, overfill detection, and pump control. Studies suggest that the industrial sensor market segment is expected to record 5% CAGR over the forecast period.

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Control Engineering Europe

INDUSTRY REPORTS

Industrial Digital Twin Association established The Industrial Digital Twin Association (IDTA) has been established by the German trade associations ZVEI, VDMA, and Bitkom, along with 20 companies from the machine building and electronics industries. The aim is to increase use of Industry 4.0 through joint development of a ‘digital twin’.

The digital twin functions as the interface for physical industrial products in the digital world within Industry 4.0 applications.

The association plans to combine the various parallel development strategies pursued to date into a globally viable, open-source solution.

This solution will provide continuous data availability across an entire product life cycle, from planning and development, production and commissioning, to use and recycling.

The solution will unlock new business models for small and medium-sized

factory equipment manufacturers and large end users from the automotive and process industries. Users will benefit from early insights into the digitalisation of industrial products and can use this information to reduce integration time and cost in their own value creation chains. In addition to the German trade associations ZVEI, VDMA, and Bitkom, companies such as Bosch, SAP, Siemens, and Volkswagen are among the founding members of IDTA. Commenting on the new user association, Dr. Gunther Kegel, CEO of Pepperl+Fuchs, said: “Standardised development of the digital twin as an open-source solution is really important for the progress of Industry 4.0. This is why I am very happy for Pepperl+Fuchs to contribute to this new user association. IDTA will now represent a significant global strategy for its members.”

Cell voltage monitoring solution for electrolytic copper refinery An electrolytic copper refinery needed to find an efficient way to monitor voltage on more than 700 electrolytic cells used to produce A-grade copper.

The refining process begins with copper anodes of 99% pure copper plates produced in a copper smelter.

They are submerged in an acidic copper sulphate solution, between stainless steel ‘mother’ plates (cathodes) in the electrolytic refinery tank house. A low voltage electric charge is fed through the tanks. Over a number of days, the copper anode dissolves into the copper sulphate solution. The positively charged copper content migrates electrolytically from the anodes to the stainless-steel mother plates where they deposit and build up into a 99.99% pure copper cathode. The impurities are separated from the copper by falling to the bottom of the tank.

The copper is then stripped from the stainless-steel mother plates and the Control Engineering Europe

copper is shipped as melting stock to mills or foundries where it is cast into wire rod, billets, cakes, or ingots, as pure copper or is alloyed with other metals.

The impurities, or tank house slimes, leftover after the electrolytic refining contain a range of other valuable minerals including gold and silver which is further processed and recovered.

Moore Industries engineered its Net Concentrator System (NCS) components to perform cell voltage Monitoring.

The NCS components included 192 temperature input modules (TIM) to measure cell voltage drop in millivolts, housed in 12 cabinets. The complete NCS system provides critical real-time performance monitoring of the cell voltages via Ethernet, helping improve overall house maintenance and management. This has allowed the operations staff to realise significant gains in production and the quality and amount of copper production.

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GRID Monitoring for Smart Cities

ART Series Unique, IP57, flexible and thin 1 kV Rogowski coil • Rated insulation voltage 1 kV CATIII • Accuracy class 0.5 without calibration • 2mm hole to pass security seal • Electrostatic shield

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EDITOR’S CHOICE

Rugged edge computers for AIoT computing The new MC-1220 series of rugged edge computers from Moxa feature an Intel Core i7/i5/i3 processor and multiple expansion interfaces. The expansion interfaces can incorporate hardware accelerators, such as VPUs, and support Intel OpenVINO toolkit for Artificial Intelligence of Things (AIoT) application development. The computers are designed for operation within a wide temperature range and are C1D2/ATEX Zone 2 certified for deployment in outdoor and hazardous

environments.

In addition, the ultra-compact fanless computers are said to be one of the world’s smallest industrial computers that come with Intel Core i7 processors, making it easy to fit them in crowded cabinets at field sites.

Control valve combats vibration, cavitation and noise Emerson has introduced the Fisher V280 full-bore trunnion-mounted ball control valve for severe and specialty pressure, flow and process control applications.

The valve provides dynamic process control by utilising a drive train designed to guide the shaft and properly absorb energy. Available attenuators for liquid and gas process fluids offer an effective solution for combatting the negative consequences of cavitation, such as vibration, erosion and noise.

The body connections of the valve have been re-engineered to simplify the design and reduce the number of parts, while utilising the same construction for the inlet and outlet. This allows

an easy retrofit of single or dual ball seals with standard or customised noise attenuating aerodomes or anticavitation hydrodomes on the inlet, outlet or both. Typical applications include midstream oil and gas, particularly compressor anti-surge service.

Monoflange valve assembly with block and bleed capability The new Ashcroft V02 monoflange provides block and bleed capability for process or instrumentation control. The process variation adds an outside screw and yoke head for service as a primary isolation valve.

Compatible with ½ to 3in standard ASME raised face and ring joint flange connections, this valve system easily mates to existing common flange couplings. 316L stainless steel wetted parts along with optional alternate materials enable its use with a wide variety of pressure media. A maximum pressure rating

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of 420 bar at 220°C ensures that the monoflange valve assembly is ready for most severe applications.

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The computers support multiple Wi-Fi/cellular connectivity options, eliminating the need for an additional wireless gateway.

Furthermore, the computing platform features TPM 2.0 technology for enhanced hardware security.

Lightweight 3D cameras

The Ensenso N-Series is a compact 3D camera system from IDS is designed for 3D applications in robotics and automated series production with IP65/67 protection which makes it wellsuited for use in harsh environments, offering protectiond against dirt, dust and water spray.

The housings of the new Ensenso N40/N45 3D stereo vision cameras with Gigabit Ethernet are made from fibrereinforced plastic which makes it very light. When mounted on a robot arm, for example, this means less stress on the robot mechanics. The housing meets requirements of protection class IP65/67.

Electronics have also been revised for the new 3D cameras: The improved infrared projector enables higher light output and has optimised heat management, which results in better data quality or higher clock rates. Power-over-Ethernet allows data transfer and power supply to be realised even over long cable distances.

Control Engineering Europe

Yokogawa Electric Corporation has developed Collaborative Information Server (CI Server) as part of its OpreX Control and Safety System family.

The solution will integrate the handling of data from plant facilities and systems to enable the optimised management of production activities across an entire enterprise and will provide the environment needed to remotely monitor and control operations from any location.

By reducing the need for travel, this also helps to lessen the risk of infection with Covid-19.

The solution automatically aggregates the data that has been acquired from plant facilities and systems so that personnel in any location can monitor and operate them and have access to all the information needed to make swift and effective decisions.

Plants operate most effectively when there is full collaboration between Control Engineering Europe

plant operators, experts in areas such as maintenance and quality management, and decision makers at headquarters, as well as with other plants. CI Server provides a remote operation environment that supports wide-area communications and allows plant operations to be monitored and controlled from remote locations. It can be used from any PC or mobile device with a web browser to monitor and control operations.

The CI Server supports a range of communications protocols, and in addition to acquiring process data from control systems, it can also aggregate data such as the operational status of facilities and equipment, raw material and finished product inventory, and energy consumption. Data on equipment maintenance, product quality, and other items are all gathered automatically in real time, converted to a unified format, and linked and associated.

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4980 €

Collaborative information server

Pyrometers. IR Cameras. Accessories. Software.

We measure temperature non-contact from –50 °C to +3000 °C. Visit:

of the new digital native workforce.

Commenting on the announcement, Peter Reynolds, senior analyst at ARC Advisory Group, said: “AI has the potential to enhance many industrial work processes; however, most companies are not wellequipped to bolt on AI themselves.

AspenTech’s industry-specific applications with embedded AI will help companies accelerate transformation. While other technology strategies require asset owners to invest in complex platforms and data scientists, with embedded AI, users can get started right away improving margins and profitability.”

Francesco Mura, digital platform manager, Process and Technology for Saras, sees the advantage for updating planning models. He said: “Aspen Hybrid Models provide efficient nonlinear planning model generation, taking information from Aspen HYSYS rigorous refining reactor models and offering a great deal of promise as a new approach for updating planning models.”

Our exchangeable microscope optics for IR cameras come with a detail resolution

Aspen Technology has introduced aspenONE V12 software, which embeds artificial intelligence (AI) across its portfolio and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety and sustainability. aspenONE V12 solutions feature the first Industrial AI hybrid model capability that is purpose-built for the process industries and other capital-intensive industries.

Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI engineering first principles to deliver accurate models at enterprise speed and scale.

The process industries have embraced digital transformation to drive operational excellence and innovation as they respond to meet the needs of growing populations and expectations for sustainability. The new solutions in aspenONE V12 address these unique challenges with better modeling accuracy, greater insights and improved total cost of ownership that can support evolving business needs and take advantage

from

AI solutions accelerate the journey to the self-optimising plant

Microscopic.

EDITOR’S CHOICE

when temperature matters

COVER STORY

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MORE CONTROL FOR OT AND IT Where is the border between automation and IT? The progress of artificial intelligence (AI), cloud technologies and simulation means that it is no longer so easy to draw a sharp line between operational technology (OT) and IT, says Andrea Rauscher, product manager SIMATIC Siemens AG and Andreas Czech, marketing manager SIMATIC Siemens AG.

T

he growing complexity of the processes, and the interdependency between systems makes it more important that developers, plant operators, maintenance staff and production management maintain full control over all processes.

A key driver of networking between IT and OT is the fact that companies are increasingly benefitting from the opportunities offered by digitalisation: Distributed engineering, plant simulation and virtual commissioning, and the use of AI for predictive maintenance are no longer niche applications. At the same time, the networking of automation components offers advantages in the context of plant-wide or even cross-plant visualisation and analysis of processes.

For this reason, even conventional automation components, such as the PLC, are now part of an extensive network.

This also increases the complexity of the tasks within the automation level.

In addition to the actual machine and plant control, the correct and secure configuration of the network is now also one of the tasks for automation engineers and plant operators.

Visualisation Using the appropriate tools, many tasks at the interface between IT and OT can now be performed without special expertise.

These tasks include simple visualisation via a web server, with which control parameters can easily be monitored and evaluated via the production network. The new firmware version 2.9 of the Simatic S7-1500 controllers, for example, provides an editor with which corresponding visualisations can easily be compiled from graphic elements without the need for special knowledge on the

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New tools enable the developer to configure visualisation solutions for the PLC in the web server even without specialist knowledge.

part of the user. This editor, like all other tools for configuring the automation solution, is part of the TIA Portal engineering software: In the project tree there is simply another node with which the user can build the corresponding web page for the CPU. This integration of the visualisation into the automation via a web server means that the user does not need special HTML coding know-how or a separate tool for the web page creation and the configuration of an interface for the data exchange between PLC and visualisation. The handling of this ‘View-of-Things” function is similar to the creation of an operator screen for the HMI system and is also fully compatible with it – for example, the visualisation of the Simatic CPU can simply be transferred to the corresponding HMI device, where it can be supplemented with advanced functions so that it can, for example, present trend views of CPU parameters in machine-level diagnostics.

Efficient mechanisms While proven concepts from automation

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can also be used for the web-based representation of PLC parameters in the visualisation, services from IT are gaining ground in the field of network management. In firmware version 2.9, the Simatic S7-1500 controllers now also support the dynamic host configuration protocol (DHCP) and domain name system (DNS), and even facilitate administration and addressing within extensive networks involving numerous stations. Thanks to OPC UA with global discovery service (GDS), OPC UA certificates can now be managed on a central server. Among other things, this simplifies the commissioning of OPC UA devices because certificates can be easily retrieved and updated via TIA Portal or via the server.

Efficient certificate management is important because of the number of OPC UA-enabled devices in automation.

The cyclic communication between OPC UA client and server, however, generates a considerable communication load, which is why users in the field of OPC UA methods should select the most efficient Control Engineering Europe

COVER STORY

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service for OPC UA communication in each case. With regards to time-critical information, messages also must be sent outside the cyclic communication.

For such tasks, the OPC UA Alarms & Conditions mechanism is ideal. Using alarms and events, the controller can send a message from the user program in the case of unexpected events without the need for a client to poll the controller and can actively inform personnel about a fault in the system. Thus, OPC UA Alarms & Conditions supplements the existing methods with a cross-vendor and cross-platform mechanism and supports existing resources in the field of plant operation and maintenance.

Although the increasing networking of OT and IT ensures greater data transparency and more efficient access to the automation level, it also has a downside: It exposes OT components to a higher risk of attack and manipulation.

It is therefore essential that users deploy appropriate security mechanisms to protect equipment, networks and systems against unauthorised access.

Manufacturers such as Siemens recommend a defense-in-depth concept in accordance with ISA 99/IEC 62443.

Accordingly, the security mechanisms in the Simatic S7-1500 controllers are continuously updated and improved: The controllers also support encrypted

communication via the transport layer security (TLS) protocol. It is, however, equally important that the user also configures and activates the mechanisms correctly and this can pose problems – for example, because standard passwords are not changed.

To support users accordingly, from the new firmware version onwards Siemens is also supplying its Simatic S7-1500 controllers pre-configured with activated security mechanisms. This ensures that no setting is forgotten or overlooked during configuration. Depending on requirements, individual mechanisms can be deactivated - this then rests on the conscious decision of the user and contributes to a greater perception of industrial security needs at the automation level.

Flexible configuration Efficient tools for engineering and configuration are important because of the growing number of networked components. The demands on the flexibility of machines and systems that are networked in this way are also increasing: Plant operators want to be able to modify processes simply by combining different stations and cells. For the automation engineer this generates the need for implementing a simple deterministic communication between

modules or machines which functions without additional communication paths.

For the Ethernet-based communication standard Profinet, the I-Device (intelligent IO-Device) function is available for this task. As an I-Device, a CPU can communicate with subordinate as well as higher-level or central controllers without the need for additional mechanisms. In this way I-Devices can be used to network and combine several modules with their own PLC – and now with Simatic S7-1500 controllers in firmware version 2.9 even to a greater or lesser extent ‘on the fly’: the I-Device functionality can now simply be activated and deactivated by command. This allows modules within a line to be switched on or off more easily than before.

As these examples show, it is important that the possibilities of automation solutions keep pace with technical development in the field of OT and IT. In this way, both machine and plant constructors and operators can benefit from the advantages offered by digitalisation, while continuing to use and further develop tried and tested concepts - and to integrate automation into the world of Big Data in a secure, controlled and smart way. !.

Simatic S7-1500 Firmware 2.9 The firmware update for the hardware and software controllers of the Simatic S7-1500 series will be released simultaneously with the new version V17 of the TIA Portal. The new firmware provides some of the Simatic controllers with memory optimisation and includes new functions that enable efficient engineering of networked automation solutions.

• View-of-Things: Standardised web-editor for all devices, HMI-like automation website programming (AWP).

• Integral IT-connectivity: DHCP/DNS, OPC UA with GDS for global, server-based certificate handling.

• Support of platform-independent notifications via OPC UA Alarms & Conditions.

• Advanced connectivity via media redundancy protocol (MRP interconnect) for larger Profinet ring structures resulting in more devices in total.

Control Engineering Europe

• More flexible line configuration of systems by activating/ deactivating I-Devices.

• Security-on-default presetting for communication and user management increase network security.

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November 2020

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DATA ANALYTICS

GETTING THE BEST OUT OF YOUR DATA

Turning data into actionable information is vital to the success of any Industry 4.0 project. Suzanne Gill finds out what data analytic solutions are available today for both process and factory applications and gathers advice about successful integration of these solutions.

M

anufacturing data produced daily in both process and discrete applications is growing exponentially. In the near-term, tremendous amounts of additional data – both structured and unstructured – will become available from both internal and external sources. Turning this raw data into useful insights requires operational technology (OT) and information technology (IT) decision makers to employ data analytics and management policies.

Marcia Gadbois, president and general manager at ADISRA, points out that there are many manufacturing use cases for this data including predictive analytics, predictive quality, demand forecasting, inventory management, and warranty analysis. “These use cases rely on both historical data, where patterns or relationships are identified among the various data points, and real-time data, where factors having the greatest effect on yield are optimised,” she said.

She went on to explain that there five main pillars that decision makers should follow to garner insights from these various data sources. The first is to decide what information is needed to foster collaborative decision-making between IT and OT, the supply chains, lines, divisions, and plants. Simply put, find the information that identifies ‘what happened’? The next step is to seek insights from the data by drawing conclusions from the sources about ‘why it happened?’ These insights help drive data-driven decisions.

The third pillar is project foresight to predict future outcomes by taking

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historical data and asking ‘what will happen next, and why’? The fourth pillar relates to data agility, or the assurance that data required by people or processes can be accessed no matter their location. It answers the question ‘how fast can the right person access the data needed to translate that information into action’?

Finally, the fifth pillar is to align data strategies with business objectives to adapt in real-time to market changes and innovation. Simply put, ‘what challenges need to be solved and where is the data to assist in making these strategic decisions’?

“To answer these questions, data analytics and data governance must be utilised,” said Gadbois. “Data analytics is the use of data, statistics, and qualitative analysis to drive decision and actions. Data governance is the practice needed to insure the management of the various data sources.

“ADISRA’s products focus on analytics in three main areas – descriptive, diagnostic, and predictive. Descriptive analytics use statistics to gather and visualise data for assistance in decision making regarding ‘what happened?’

Diagnostic analytics use statistics to find valuable insight to answer the question ‘why did this happen’? Finally, predictive analytics uses statistics to forecast ‘what will happen next’?”

Gadbois points out that, in the manufacturing data analytical journey, there are several necessary steps. First, define the problem to be solved and find the data required to solve the problem, whether it be historical, structured, or unstructured. Do not be

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afraid to pull the data out of the legacy systems and integrate the data into a new source. Despite having a long-term vision for your analytical journey, start small while continuing to grow the project. Identify the stakeholders and how they define success. Get buy-in from the business on what they want to see and use data governance to keep the business involved.

Finally, measure in quantified ways and continually improve on the model as new information becomes available.

Data, people and analytics Michael Risse, CMO & VP at Seeq Corporation, cites three critical aspects for success when moving from data to data-based decision making in Industry 4.0 initiatives. These are data, people, and analytics. He said: “First and foremost is the data and ongoing access to it – in both legacy systems and industrial applications – because the best laid strategies will be tweaked and improved in the course of Industry 4.0 initiatives. No battle plan survives contact with the enemy!”

According to Risse, the ability to start an advanced analytics project with the data where it is, in silos and different systems and of various types, is absolutely critical. “Plans beginning with assumptions about what data will be required, prerequisites for data movement or aggregation, summarisation are not compatible with the inevitable required changes and tweaks. Agility and adapting to change are the core of Industry 4.0, so starting with fixed expectations and expensive data transformation efforts Control Engineering Europe

DATA ANALYTICS mapping, AI, and billing systems,” concludes Risse.

Breaking down analytics According to Elinor Price, senior business development leader, Life Sciences and Specialty Chemicals at Honeywell Process Solutions, in the simplest form, the four types of analytics seen across manufacturing today are: • Descriptive analytics: What happened?

• Diagnostic analytics: Why did it happen?

• Predictive analytics: What might happen?

• Prescriptive analytics: Recommends action.

InsightView and KnowledgeView provide visual dashboards that help users diagnostically drill down into the root cause of problems, and provide consolidated results of predictive modelling to help forecast machine performance.

before the benefits and proof of value from the achieved insights is the wrong way around.”

Risse says that a consistent finding in successful Industry 4.0 projects is the recognition and leveraging of employees’ skills. These people know the plants, processes, and procedures.

In practical terms this means bringing innovation and abilities to current employees, which results in an increase in the organisation’s overall capacity for driving improved outcomes because insights and abilities are distributed, versus centralised far from the point of action.

This insight may sound counter to all the attention paid to data scientists and machine learning. But, according to Risse, what the hype about data scientists misses is the fact they don’t know the plants, the assets, or the first Control Engineering Europe

principle model of how plants run – so their ability to find insights of value in a changing environment of raw materials, prices, and schedules is limited. The employees who know the plant best, on the other hand, know just what they need for improved outcomes, they just need improved advanced analytics software for easier and faster insights.

Finally, with access to the data and the right people, it is time to bring them together and deliver innovation to those with the greatest abilities and needs. “Therefore, the imperative is bringing data science and innovation in analytics to the front lines of the workforce. Advanced analytics applications must wrap up and make accessible the innovations behind the scenes of the software, like the Google search bar wraps the MapReduce algorithm, or the Uber app integrates

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Price explains that the most common analytic – descriptive – is done every day, across every business. This is the summarisation of the existing raw data by using business intelligence tools to explain what is going on. This step makes raw data understandable to explain what happened. The most common techniques are data aggregation and data mining of the historical data.

“Looking into past performance to determine what happened and why something happened is the role of diagnostic analytics, which is used in principle components analysis (PCA), attribute importance and sensitivity analysis,” she said. “Switching from the past to the future leads to predictive analytics, where probabilities of occurrence of an event are forecasted using statistical models and machine learning. Descriptive analytics is the foundation of predictive models. Data scientists work with subject matter experts to tune these models for better prediction. Some of the newest analytics technologies being applied are machine learning algorithms, for example, advanced pattern recognition.”

Price continues: “The most advanced analytics type is prescriptive, that recommends one or more courses of action on analysing the data.

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DATA ANALYTICS

Seeq enables subject matter experts to directly and visually interact with the data of interest to create insights.

Prescriptive analytics can recommend all favorable outcomes based on a specified course or action or can suggest various courses of action to attain a specified outcome.

Offering advice on successful integration of analytical solutions price said: “For any analytics data is paramount. If a holistic view of manufacturing is desired, it is important to break down data silos and combine data from disparate data sources into a single environment such as a data lake. Structured and unstructured data from the manufacturing process, equipment and business can all be stored in data lakes together, enabling increased insight across a wide array of stakeholders.

“When data is collected and stored across all the different systems as variables, attributes, measurements, events, etc., data contextualisation becomes a critical consideration.

Any data contextualisation is the organisation of related data collected using metadata, which provides data about data. Contextualisation is important for providing a broader understanding of the pieces of

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data which are critical for analysis, aggregation, models and interpretation of that data.”

Key values Jim Chappell, head of AI and Advanced Analytics at AVEVA, says that the greatest obstacle to realising Industry 4.0 is not generating and collecting data itself, but extracting value from that data. “The key value comes from data analytics solutions which provide context to large, complex sets of data,” he said. “Data is aggregated from previously inaccessible and disparate sources into a single source of truth.

Through advanced data analysis and visualisation – using machine learning and advanced pattern recognition – actionable insights can be extracted.

These analytics tools make it possible for people to take insightful and information-driven action to identify and solve problems at their source, before they compound into critical failure points that cascade into further problems.”

According to Chappell, predictive analytics solutions are among the most common tools adopted by

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enterprises embarking on a process of digital transformation. Existing historical data is analysed to understand an asset’s operational behaviour.

Advanced pattern recognition and machine learning are then deployed to monitor the asset in real-time and identify anomalies in how the asset is performing. Potential operating issues are then identified, diagnosed and remediated days or weeks before failures can occur.

Prescriptive analytics solutions add another layer of sophistication to predictive analytics. Once anomalies have been identified, these tools assess the potential impact and prescribe the most efficient action to prevent asset failures. Beyond that, prognostic analytics leverages more advanced AI to assess the future state of things, such as forecasting the remaining useful life of an asset. Combined, these data analytics solutions enable organisations to predict asset failure, assess risk, and then prescribe the most economically advantageous actions to remediate potential asset failures.

“The quality of the data is integral to the success of Industry 4.0 projects,” Control Engineering Europe

DATA ANALYTICS continued Chappell. “Digital tools that use artificial intelligence capabilities, such as machine learning, are only as good as the data under analysis.

Underlying the ability to execute a successful analytics strategy is the ability to manage and curate data to ensure quality, integration, accessibility and security. Historian data harmonise and integrate multiple sources of data and ensure it is cleaned, accurate and structured to be analysed effectively.”

Chappell advises that businesses should start their digital journey by implementing one or two digital solutions which can be modified and supplemented with others.

Taking advantage Shahin Meah, senior director, digital transformation and lifecycle services Europe at Emerson, says that customers are very interested in taking advantage of analytics to create production, operations and plant-level benefits.“Typically, they want to apply analytics to increase reliability, lower energy consumption, increase quality, and ensure safety,” he said.

“Data, and better yet, actionable data, can enable companies to bring industrial facilities to life with dynamic sensor and analytics networks to detect potential problems before they impact production or risk the safety of plant personnel. Plant workers

are armed with the real-time insight to proactively assess the integrity of operating equipment, and target maintenance that minimises risk while ensuring business continuity.”

According to Meah, data-driven analytics, which predict behaviour from statistical analysis, should be familiar to those tasked with making operational improvements. This form of analysis has been deployed for many years, but we have seen an exponential rise in computing power, while data storage costs have reduced, and algorithms have become much more sophisticated.

We now have the capability to use machine learning within this analysis that can remove the need to programme everything a machine does.

He said: “Operational analytics – with embedded domain knowledge can impact and improve performance of simple equipment, complex assets, process units, and entire production plants – can present a massive opportunity for manufacturers and processing companies.

“Failure mode effects analysis is another form of principles-driven analytics which is used widely to predict or prevent over 80% of known failure modes. Emerson, for example, has built almost 500 failure mode effects analysis models for the common assets found on a plant.

We know the data to collect and the

algorithms required to interpret that data into actionable information. End users simply need to decide whether it is worth making an investment to obtain the data and establish a digital repeatable way of making improvements.”

Meah points out that it is essential for processing and manufacturing plants to leverage existing infrastructure, systems and instrumentation in order to achieve scalable success when integrating analytic tools. This requires the use of analytics solutions that are designed to securely connect and extract data from legacy systems and instrumentation. It is also important to have secure access to field device data residing within the process control system and any newly added monitoring and optimisation hardware and software.

The NAMUR Open Architecture (NOA) is a standard system architecture specifically designed to support digital transformation initiatives without compromising plant cybersecurity (availability, integrity, confidentiality) and safety. NOA adds to the existing automation architecture and is based on existing standards such as fieldbus protocols and standard software application interfaces (API), which enable less complex integration of digital components from the field level up to the enterprise level. !.

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DATA ANALYSIS

FOLLOWING THE SCIENCE FOR GREATER PRODUCTION EFFICIENCY To get the most out of data that already exists in the production environment requires cooperation between data scientists and production experts.

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trategic data analysis is gaining momentum in the production environment. Frost & Sullivan believes that data analysis in the industrial sector has immense potential – production efficiency could be increased by about 10%, operating costs could be reduced by almost 20% and maintenance costs could be minimised by 50% utilising data that already exists in the production process.

The issue faced by many factories today, however, is that while the data is easily collected and stored, little happens after that, so insights that are hidden in the available information are lost. There is often also a lack of budget and personnel to devote to the task of analysing data.

Channelling the flood of data and extracting value from the information collected by sensors, controllers

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and machines is a complex task which involves more than standard statistical methods and tools. Manual evaluations and the creation of dashboards and reports are not enough, as dashboards will become increasingly complicated as data volume expands. They don’t show relevant information at the right time so that an operator can see at a glance what is going on and take action.

While the routines implemented in a normal machine control system for monitoring production processes and detecting errors can identify current deviations and problems, they are not able to predict future problems, link information in a meaningful way and perform advanced analysis.

Close cooperation The central task of data analysis in Industry 4.0 scenarios is to extract

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decision-relevant information from collected data and present it to the right user at the right time.

This involves planning the process of converting data into useful information in a conscientious and well-founded manner before implementing it. The process requires close cooperation between data experts (data scientists) and specialists in production processes who know the story behind the data.

Data scientists will be familiar with the ‘3 V’s’ of large data sets – Volume, Variety and Velocity. A modern packaging machine, for example, can easily generate gigabytes of data per day that can be stored over a long period of time. For inspection machines, the systems may generate many terabytes each day. Storing this amount of data is not a problem, but using it is a challenge. Further, the Control Engineering Europe

DATA ANALYSIS type of data provided by machines today is much broader than it was a few years ago – measured values stored, as well as raw information from sensors and other metadata. It is not only about maintenance results, but also associated images. Additionally, data can be generated by the machine operator. This includes cycle times and even written and spoken feedback.

Raw data from sensors is typically read every millisecond and must be treated as streaming data.

Concurrently, the speed of data analysis is playing an increasingly important role. As such, updating the dashboards once a day or every hour is not enough. An operator needs to be informed about potential problems immediately to avoid downtime. Ideally, the machine should be notified in real time so that it can automatically correct itself within the same product cycle. In addition, data may be corrupted due to a problem

in the sensor or other device, it might go missing or it could be recorded in an outdated manner. Because these scenarios can seriously compromise analysis and lead to false conclusions, data scientists must continually check the veracity of the data.

Data science Industrial data science is a relatively new discipline, which is why there is no broadly valid approach that is suitable for every company. Every solution and application requires customised data analysis and modelling to achieve the best possible result. However, a standard approach is useful. The CRISPDM model, (Cross-Industry Standard Process for Data Mining) is the most commonly adapted basis.

A data-driven solution does not always have to include complex machine learning models or artificial intelligence. Sometimes, effective data processing to provide the right

information at the right time in the right way can be enough.

Developing the potential of data in your own production environment is no small task, but it is worth doing. In today’s manufacturing environment, it is no longer enough to simply collect data and build a few graphs. Instead, filtering out production-relevant information from the data and presenting it to the appropriate audience in the right way is vital. The key is to transform data into useful information. This is best achieved in close cooperation between data scientists and experts in the production process. Only then can a solution be developed that is popular, often used and generates long-term value. !.

A whitepaper from Omron offers further information on how to benefit from the full value of industry data.

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ROBOTICS

BENEFITS OF AN OPEN SOURCE LANGUAGE FOR ROBOTS

Neil Ballinger explores the open source Robot Operating System (ROS), which he believes will become a common language for industrial automation.

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he open source Robot Operating System (ROS) has been around for over ten years and today there are thousands of developers building packages for it. A 2019 report from ABI Research, stated that, by 2024, almost 55% of the world’s robots will include a ROS package.

Developed in 2007, at the Stanford University Artificial Intelligence Laboratory, ROS is a layer of middleware that can run on several operating systems or without one.

Although it is not an operating system (OS) ROS delivers the services that would be expected from an OS – such as hardware abstraction, low-level device control, implementation of commonly used functionality, messagepassing between processes and package management.

The software in ROS offers flexibility.

Organised in packages, it can contain nodes, datasets, configuration files or anything that constitutes a useful module. This makes it suitable for several robotic applications and even has the potential to advance its capabilities on already established industrial hardware in factories.

Traditionally, when a robot is added to an operation, it is set up with the closed-source software that comes with the robot hardware. This proprietary software, of which the software’s publisher or another person retains intellectual property rights, offers modules that allow robots to adapt to dynamic environments.

For example, it is applied to automated guided vehicles (AVGs) to avoid collisions with nearby machinery in warehouses, and pick-and-place robots for changeable motions to manipulate objects and execute

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preprogramed tasks. However, these adaptive functionalities are often quite limited.

Open source benefits Unlike proprietary software, opensource software gives users full access to the source code. If a plant manager wants to customise a robot’s programme, then the developer not only has access to the code, but is able to edit it within the licensing agreement. In fact, ROS-based code engineers, from the ROS-Industrial program initiated by Southwest Research Institute (SwRI), are using open-source software for nonpreprogramed robotic actions.

SwRI researchers recently developed a module that lets two robots collaborate on a handling task. The function of these robots is based on point-cloud data from cameras imparting stereo vision and depth perception for environmental understanding. Therefore, the robotic application’s possibilities with ROSbased code is endless, as manufacturers

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could, for example, install 3D cameras around a work cell to gather data on interactions between robots and the parts being worked on.

Minimising obsolescence Engineers will also be pleased to hear that ROS uses the internet’s standard Transmission Control Protocol/Internet Protocol (TCP/IP). This means that new robotic hardware can be installed on a production line without rendering other parts of the system obsolete, and that all the equipment will still ‘talk’ to each other, without the need for costly reprogramming.

Since its creation over 10 years ago, ROS has become a widely used platform across various robotic applications. It is because of this that there are now thousands of developers working with ROS to build the future of robotics, allowing manufacturers to reduce obsolete equipment and customise a robot’s program to their liking. !.

Neil Ballinger is head of EMEA at EU Automation.

Control Engineering Europe

UK INDUSTRY REPORT

A RECORD FOR ROBOTS IN UK FACTORIES The new World Robotics 2020 Industrial Robots report, presented by the International Federation of Robotics (IFR), has shown a record number of around 21,700 industrial robots operating in factories of the United Kingdom – an increase of 5%.

Despite the increase it would appear that robot use in the UK still has some catching up to do. “The UK has a surprisingly low robot stock for a Western European country in the manufacturing industry,” said Milton Guerry, president of the International Federation of Robotics. “Though the UK´s operational stock hits a new record, other European countries like France, Italy and Germany have between two times and even ten times the stock in operation. The automotive and the general industry need to invest in automation technology to keep up with international competition.”

The automotive industry is the largest user of industrial robots in the UK. At the end of 2019, this industry accounted for 52% of the total operational stock of robots (11,000 units).

With 13% of the operational stock, the plastics and chemical industry was the second largest user of industrial robots (2,710 units).

IFR says that even without the coronavirus, investments in the UK were already dampened because of the unclear Brexit situation and that, if no trade agreement is found by

the end of this year, the UK will be treated like a third-party country of the EU. While the IFR says that this will inhibit the modernisation of manufacturing production facilities, it will also determine the speed of economic recovery after the pandemic. Brexit might, however, drive robot installations in the UK because immigrants from Eastern Europe are starting to return to their home countries and government policy is to restrict immigration. These immigrants often worked in low-wage jobs, particularly in the food industry as well as in other manufacturing jobs, and might not be easily replaced by human labour in times of low unemployment.

COLLABORATIVE ROBOTS COULD HELP DELIVER VACCINES AT SCALE

When, or if, an effective Covid-19 vaccine is discovered, the next major challenge will be to scale up its development, clinical trials and manufacture to unparalleled levels says the Cambridge-based industrial design and product development partnership between Design Momentum and Plextek.

The two companies are working together to develop technology for a new range of lab-based collaborative robots to improve scale-up capabilities There is a pressing commercial need for new collaborative robots that can automate and semi-automate many of the manual processes involved in handling a variety of consumables. The process will also support Phase 2 and 3 clinical trials, serving to accelerate the time to market.

Design Momentum and Plextek are developing technology that uses a multiple-sensor configuration, rather like those found in Tesla cars, that monitors the environment around it and responds appropriately. The team is also looking at ways of creating improved human Control Engineering UK

interfaces, which include wearables and technologies such as AI algorithms to support the processing of a wide range of reagents and cell culture consumables. One of the problems of existing semi-automated systems is that they are often tailored to specific consumables from a given manufacturer.

The human interface is vital as connections such as feed and drain lines are made manually, and the process of seeding and harvesting and the monitoring of overall cell health is overseen by cell culture professionals.

Design Momentum and Plextek are engaging with other biotechnology companies, including Clinical Research Organisations (CROs) and Clinical Manufacturing Organisations (CMOs), to contribute to the development and provide end user experience. “This new generation of collaborative robots will enable safe and efficient processing of a wide range of reagents and consumables in a user friendly and reconfigurable format,” said Stephen Guy, design principal at Design Momentum.

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November 2020

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MACHINE SAFETY

SAFETY OVER NETWORKS: WHERE CONNECTIVITY MEETS PRODUCTIVITY Dr Martin Kidman discusses the issues relating to machine safety in an era of greater machine connectivity.

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s Industry 4.0 machine connectivity grows, so does the potential to make automated production and material handling processes more flexible and responsive. Manufacturing and logistics environments still require interaction between humans and machines, but the physical barriers between them are being gradually removed, creating more dynamic ways of working.

Machines need no longer be rooted to the spot, separated from each other and from humans. Autonomous vehicles automatically change their paths to avoid obstacles, production areas are frequently reconfigured, machine settings and tooling must be changed rapidly to accommodate product variation.

These modern, dynamic production environments are founded on bidirectional communications networks between machine controllers and a range of devices. Connecting machines over networks has brought many advantages by dramatically reducing wiring and increasing plant autonomy.

Bi-directional communication also offers the opportunity for diagnostic insights and other simultaneous data exchanges, right from the heart of machines.

The safety challenge But how do you ensure this more responsive and interconnected world remains safe for the humans working in it and how can the safety of the machines and the wider systems they are part of be kept inherently safe?

The possibility to transfer safe (as opposed to ‘non-safe’) data over networks has only really been possible since the turn of the 21st century when the IEC 61784-3 standard was published.

It covers functional safety fieldbuses and

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gives the rules and profile definitions of adding a safe data layer on top of existing fieldbus protocols.

A key advantage of implementing a safe network is that wiring is dramatically simplified by communicating over the same fieldbus system. Traditionally, a safety system has needed many individually-wired connections with added, in-built redundancy. With complete safety information about different devices at their fingertips, designers can also configure and reconfigure complex

safety systems without the need to redraw wiring diagrams every time.

Safety device manufacturers have introduced their own proprietary safety protocols. SICK’s EFI (Enhanced Function Interface), for example, enables fail-safe communication using the ‘Black Channel’ approach allowing the transmission of both failsafe and standard data on the same bus system.

Using EFI, safe communication has a very low probability of dangerous failure and does not normally reduce a system’s integrity, enabling performance levels of up to PLe (EN ISO 13849) and safety integrity levels of up to SIL3 (IEC 62061) to be maintained.

The EFI interface was initially a linear

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bus system enabling communication between four SICK devices. Then, the release of the SICK Flexi Soft EFI-pro Gateway enabled open and safe integration via EtherNet/IP CIP Safety and allowed connection to devices like the SICK microScan3 safety laser scanner as well as to third-party robot controllers, remote I/O modules and safety PLCs. Connecting a safety laser scanner to a safety controller over EFI Pro enables designers to access all safe data over one cable to create adaptive, scalable modular safety. Field switching, adding multiple scanners or connecting to EtherNet/IP-enabled robots, encoders and other devices, no longer requires multiple cables and programming tools.

The system enables safe human and robot collaboration with minimal effort. Especially where speed and distance are issues, it offers intelligent and responsive safeguarding for situation-dependent robot protection.

With Safe EFI-Pro, Automated Guided Vehicles (AGVs) and carts can work more quickly, intelligently and safely.

The SICK microScan3 EFI Pro safety laser scanner offers storage of 128 Individual fields and simultaneous field evaluation of up to eight protective fields, with up to a 9m range. Simultaneous protective monitoring of multiple fields means less need for switching between monitoring cases, so dynamic protective fields can be shorter, and therefore more responsive and efficient. The safe contour detection field supports applications such as safe AGV docking and protecting workers at narrow access points as well as providing signals for self-muting. !

Dr Martin Kidman is a safety specialist at SICK UK. Control Engineering UK

Be safe and secure with Pilz Don‘t leave Safety and Security to chance! We protect your plants against unauthorised access and your employees from hazardous machinery. Our solutions cover authorisation and authentication, reliable guard locking of safety gates during operation and protection against manipulation of the control network. Play it safe with solutions from Pilz.

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NEW PRODUCTS

Protecting controllers from manipulation and unauthorised access If people, machinery and industrial processes are intelligently linked, networks are also more susceptible to attack.

The Pilz SecurityBridge hardware solution is designed to protect the configurable and safe PNOZmulti controllers and the PSS 4000 automation system from network-based attacks and unauthorised access.

The SecurityBridge protects the connections between the programming/ configuration tools and the hardware controllers from manipulation by detecting unauthorised changes to the automation project, for example. It acts as a firewall, but unlike generic firewalls, no complex configuration is needed.

Thanks to application-specific default settings the device is easy to commission using the plug-and-play principle.

As well as benefiting from the security aspect, users will also enjoy higher plant availability because only the data that is necessary (authorised configuration and process data) is transmitted.

Angle seat valves enable high and constant flow The ASX series of pneumaticallyoperated angle seat valves from Camozzi Automation are designed to control steam, liquids, gas and fluids that contain suspended solid particulate matter.

The valve design is said to enable a high and constant flow, while ensuring a low pressure drop. A variety of options are available with regard to nominal diameter, type of fluid and process connection type.

Made entirely of stainless steel, Series ASX offers a suitable solution for a variety of industrial applications where, in addition to high flow rates, the ability to control viscous fluids is also a requirement. Potential user environments could include steam production and water treatment

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plants, through to the food and chemical industries.

The two-way direct acting valves are actuated by a pressure piston and have a Y-pattern body which enables a highly efficient flow rate, the seat angle being designed to enable the maximum possible flow when the orifice is open.

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Simplified valve system commissioning and configuration

Emerson has introduced a wireless automatic recovery module (ARM) for its AVENTICS G3 electronic fieldbus platform which is designed to make it easy for technicians to perform pneumatic valve system commissioning and diagnostics from a mobile phone, tablet or laptop computer.

The AVENTICS G3 fieldbus platform is said to be the first in the industry to offer wireless technology that puts valve system configuration and diagnostics at the control engineer´s fingertips, helping manufacturers reduce production downtime and simplify valve system commissioning, while creating a path for using diagnostics/ prognostics for analytics and advancing our offering of intelligent devices with IIoT capability.

The wireless ARM module provides easy access to the diagnostic and commissioning capabilities of the AVENTICS G3 fieldbus platform via an internal Wifi access point and mobile website. The wireless ARM module generates error notification for alarms, voltage levels, short circuits, module errors, open load errors and distribution errors to reduce system downtime.

The wireless ARM module is compatible with Ethernet/IP DLR, and PROFINET protocols. Additional protocols are targeted for future release.

Control Engineering UK

ROBOTICS

COBOTS: FINDING THEIR PLACE

Suzanne Gill asks where cobots might offer the most value in today’s manufacturing processes and finds out what work is being undertaken to ensure their trustworthiness.

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ccording to Nicolas Lauzier, senior product manager at Robotiq, there is an increase in the use of cobots across a broad spectrum of applications, due to their flexibility. He said: “While companies may use standard industrial robots for their day-to-day operations, the deployment of cobots is burgeoning in applications where greater flexibility is needed – but not at the expense of volume or performance. This is particularly the case in applications where products are currently being handled manually, meaning significant alteration or reconfiguration of the physical production environment is not needed.”

According to Lauzier, one of the key factors behind the growth in cobot deployment is the ready availability of simpler and more cost-effective systems comprising standard hardware, tooling and ancillary components. These, he says represent an attractive alternative to larger, fixed automation systems.

Palletising choices In the area of palletising, for example, the robotic automation choice has traditionally been between ‘bundled’ systems – comprising a range of components from different suppliers packaged as a standard solution – or working with a supplier to create a bespoke solution based on an arbitrary productivity target. “Bespoke solutions often result in extended development and testing time, creating a system which may be challenging to reconfigure or redeploy should requirements alter in the future,” continued Lauzier.

“However, using standard products for a cobot application allows for more Control Engineering Europe

rapid simulation of the application – its feasibility, predictability, likely throughput and payback time. Many providers now offer online configurators which allow an analysis to be undertaken based on part dimensions, weight, required throughput and so on. This approach also allows for more rapid risk assessment compared with more complex systems comprising components from different suppliers where there may be no universally compatible software. Here, any alteration to the application will require a completely new risk assessment, at potentially significant expense and delay, to assess the effects of the new application on the production environment and those within it.”

Lauzier offers a typical cobot application example – in a food & beverage or consumer goods facility – where standard products manufactured all year round are handled on specific palletising lines by larger, in-situ robot systems. Here, occasional or specific applications – for example Christmas goods, or items specific to one customer, with a different box dimension – can be undertaken by a standard cobot system

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which can be rapidly reprogrammed and redeployed once that particular requirement has been fulfilled.

He offers another example that demonstrates the value of cobots, in bin-picking for machine tending applications in the automotive sector, where components for different vehicles may be similar but not exactly the same.

“Here, investment in a bespoke part feeder would not be appropriate – even if this could be altered for other parts, it may be a lengthy process. But standard software can be used to reprogramme a cobot to handle components of different dimensions without significant downtime,” he said.

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ROBOTICS Working together Oliver Giertz, product manager for servo/ motion and robotics for the EMEA region at Mitsubishi Electric, agrees that cobots offer a good solution to meet the current need in manufacturing for more flexible systems, driven by the rise of high-mix low-volume production. “By working together with humans to enhance new and existing production lines, cobots can help companies meet this demand,” he said.

Designed to assist human operators on the shop floor, cobots can take on simple, repetitive and physically strenuous tasks. This offers improved consistency and reliability in manufacturing while enabling humans to concentrate on more complex jobs. A cobot is also flexible and can quickly learn to adapt to a variety of tasks – from picking and placing to machine tending.

According to Giertz, the line between industrial robot and cobot is becoming less defined. He said: “Some industrial robots can limit their speed, range of movement or torque when safety sensors are activated, allowing operators to move into the same workspace without physical guards. The robot will slow down and then halt when the human, or other moving object comes too close. A key attribute of the cobot however is that they can continue to operate alongside humans, including physical manipulation by the human operator – in this case the pause in motion is momentary.

“When deployed for working alongside humans, cobots don’t require physical safety guards. This means that production lines can benefit from increased automation in a more compact space. This also requires a greater reliance on safety functionality.” He goes on to point out that recognised standards, such as the TÜV safety certification for the ISO/TS 15066:2016, are being used to clearly define this. Overall safety processes must also be in place to limit access to programming functions, such as protecting settings with a password and log of the time as well as date.

Additionally, appropriate risk assessments need to be conducted before working

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with a collaborative robot.

Giertz believes that a key area of value for cobots within manufacturing processes is their ability to work flexibly across a variety of tasks. “Frequent redeployment means that they need to be fast to set up, without requiring robotic programming expertise. A new development which can improve the ease and speed of configuration is handguided teaching, where the cobot arm is simply moved to the desired position and added to the operational sequence at the press of a button. This technology means that no complex programming is required, it can also be combined with touch-screen graphical interfaces to implement more sophisticated operations.

“A digital twin can also be used to enhance the speed of set-up and redeployment. Employing a digital representation of the physical cobot, including the virtual capability to evaluate performance, the digital twin can simulate cobot interaction to ensure that it can safely and predictably fulfil the desired task before deployment.

Difficult environments For basic, simple and repetitive tasks cobots can be the perfect choice of worker. Ian Hensman of Kawasaki Robotics (UK), points out that this is especially true if the working environment is hostile, space is at a premium or guarding causes access issues. Likewise, if the materials or

products being handled are not pleasant to work with or are potentially hazardous. “Here, a cobot just gets on with the work, 24/7 if need be, without a break and will do things exactly right first time, every time,” said Hensman.

However, he goes on to point out that having cobot technology and continuing to develop it is one thing, but gaining wider acceptance of the principle of employing cobots is quite another. “I don’t think that many of us involved in cobot technology will dispute one thing: that customer and user perception is sometimes hard to overcome, despite the maths showing clear financial advantages for the immediate deployment of cobots. It is an odd ‘social’ viewpoint.”

Hensman believes that the future of cobots depends on how the terms of reference for a cobot evolve and are interpreted. For example, which tools are acceptable to remain a cobot and which tools might turn a cobot into a robot operating under a completely different set of rules for the workplace? “Our technical, engineering and industrial past shows that the widespread acceptance of some new technologies can take a very long time and is often far from being a straightforward path. There is a need for those in the cobot business to continue to present a united front to gain acceptance and to persuade potential users that that cobots have a vital part to play in so many applications and environments,” concludes Hensman. !.

Seven-axis lightweight cobot for industrial tasks Kassow Robots has introduced the KR1018, a new 7-axis lightweight cobot that is able to handle payloads of up to 18kg and which has a reach of up to 1,800mm and with a speed of up to 225 degrees per second .

The cobot weighs just 34kg – a

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feature that enables it to meet a fundamental requirement for flexible automation solutions, especially in narrow spaces.

The KR1018 can perform tasks such as loading and unloading of heavy parts for the metalworking industry or tasks such as heavy lifting in the foodproduction sector.

Control Engineering Europe

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DIGITAL TWINS

PROCESS SIMULATION – WHAT CAN A DIGITAL TWIN DO? Eckard Eberle discusses the role for model-based technologies as the basis for continuous process optimisation and efficient plant operation.

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magine you could see the future when operating your plant. Not with a crystal ball or cards, but with absolute certainty. The key to achieving this lies in simulations that are very close to reality. In a model such as a digital twin, the reality – in this instance comprising all the plant components and their characteristics and functionalities – is described using mathematical equations. But the digital twin can map not only reality, but also ‘what-if’ scenarios to provide reliable predictions of a plant’s future behaviour. It can do this because the model is bound by neither the technical limits of a device nor safety restrictions so the work of process optimisation can therefore be completed in a safe, virtual environment.

And because the simulations have no time limits, accurate predictions of the plant behaviour are no longer a

ipipe dream. In an ideal scenario, three ‘independent’ digital twins collaborate on a single process plant: The digital twin of the product, the virtual image of the production plant, and the digital modeling of product and production performance. To understand the concept of a ‘digital twin’ it is important to remember that the depth of detail – the accuracy of the twin – is highly dependent on the intended purpose.

Depending on the specific job, for example, production simulation, optimisation of the production process, or solving economic problems, models of greater or lesser accuracy are required and useful.

A new approach New perspectives are now emerging from the approach of integrating the individual models and software tools

into a consistent, semantically linked system over the entire life cycle of a plant. A great deal of in-depth process engineering expertise, combined with many software systems, is obviously required to produce these types of systems and models. It is therefore possible to use existing system knowledge and the latest published information to produce an initial digital process twin using simulation software.

This is used to design the plant and its components – the so-called ‘conceptual design’. In this phase, all knowledge flows into a process flow diagram (PFD), the basis of the digital process twin. In the next engineering phase, the digital process twin is transferred to the system planning tool to form the basis of the digital plant twin. This is sequentially extended by further system-specific elements. The basic structure of the

Improving manufacturing process development Optimising the entire engineering chain is becoming ever more important. Since the potential performance and availability of machines, in terms of overall equipment effectivness (OEE) management, are already being effectively utilised, there is a need to search for competitive advantages elsewhere.

Optimisation of the engineering chain by using digital twins looks like a very promising area to find improvement opportunities, according to Piotr Siwek, head of product marketing EMEA, Factory Automation at Mitsubishi Electric.

Mitsubishi took this approach in its Nagoya Works factories, where it has investigated the potential benefits of this approach in multiple areas. For example, by linking data between various engineering tools it has reduced the design time for new applications. It could also decrease the time necessary for the physical implementation of digitally designed systems and could cut the maintenance time needed for existing assets.

While developing these solutions Mitsubishi has learnt a great deal about the potential areas of improvement in manufacturing facilities. An example of this in practice today

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is the use of software by engineers to perform mechatronics and structural debugging on newly designed production processes. Equipment is verified and results are instantly fed back to designers. In this way vast amounts of data, using the right methodology, can enable the continuous improvement processes.

As a result, the start-up period for new production activities on some sites at it Nagoya Works, which includes on-site verification and the process of starting machines, has been reduced by 40%.

When it comes to maintenance, the company has implemented several similar technologies. For example, it has developed a solution that is able to record equipment abnormalities in a new way. It is possible to see information on the behaviour of various components in combination with video streams of a whole machine or crucial process components. This gives a better understanding of what could happen on-site. These data are used as input information for design and simulation software, which enables and speeds up the improvement of machine design.

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Control Engineering Europe

DIGITAL TWINS digital plant twin can then be easily created using plant engineering tools.

Once the process engineering is complete, all the required information is transferred to the engineering system in a process control system. In addition to this, the field level is mapped in a simulation platform so that the automation software can then be virtually commissioned. Virtual commissioning has many benefits – on the one hand, all the automation functions can be thoroughly tested in advance while the simulation can also be used to prepare and train plant operators, particularly for critical scenarios which can be played out in the virtual environment without any risk to the actual plant. Training is therefore provided for both standard operation and plant behaviour in the event of disruption.

More than reality Soft sensors are an important application of a digital twin in the

operating phase. They estimate process variables which are not actually available using a process model to optimise process control as required.

Ideally, this model is the existing digital twin of the plant. Previous experience has shown that model-based technologies are the key for simulations, process optimisation, and even for accurate forecasting. Consistent use of the three digital twins of product,

production and performance mentioned earlier maximises the economic benefits over the entire life cycle of a process plant. These benefits can be significantly increased if the simulation models are not recreated at each stage but are interlinked or transferred within each other. !.

Eckard Eberle is CEO of Siemens Process Automation.

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DIGITAL TWINS

RISE OF THE DIGITAL TWIN! Steve Sands explains how and why the digital twin is set to be a key technology for the success of Industry 4.0.

T

he Industry 4.0 vision of a digitally connected world relies on smart products communicating with one another and the outside world through the Industrial Internet of Things (IIoT).

The concept requires a standardised approach to the description of these components and how they interact and relate to the other components or assets to make a smart machine. This precise, digital model is called the administration shell, or digital twin.

The digital twin precisely describes the asset in a standardised and structured way. Each asset needs to ‘know’ its characteristic functions and features and how it fits into its environment – its relationship with other smart assets.

The importance of the clarity of this information becomes clear when you look at the three-dimensional RAMI model and the relationship between the different elements of the product life cycle, the asset intelligence and the control hierarchy. The digital twin is a standardised and machine-readable digital product ‘label’ that enables the seamless flow of information throughout this three-dimensional model.

A term frequently used regarding the digital twin is ontology. It has been taken from philosophy and in this context is used to describe how a smart asset describes itself and has a sense of ‘being’ – what it is, what it does, its physical characteristics. Having this information embedded within the product or stored in the cloud and intrinsically linked to the product, provides the digital twin. This is the basis for smart products working in relationships to create smart machines and, when combined, smart factories.

Potential benefits The potential benefits of the digital twin approach are enormous – reducing machine design and build times,

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optimising performance throughout the machine’s operational life and having a key role in condition monitoring, predictive maintenance and smart maintenance. It enables product data to be merged with application information to provide data-driven insights and increasingly, through AI to predict future performance.

From the initial design stage savings can be made.

For example, pulling digital twin information into an The Festo Handling Guide Online helps customers to early-stage design concept find the right handling system for their application and enables the construction of generates the relevant documentation. a virtual 3D CAD model. This ProStep, etc. The eCl@ss standard can be programmed and the defines how products are described operation and performance of the system in terms of both engineering and evaluated without any metal being cut or production, which is divided into assembled. Being able to seamlessly pass two parts – machine and product, information between functions such as packaging and transport logistics). mechanical design, electrical engineering, It also takes into account the control and software departments hierarchy of assets to provide an asset eliminates the wasteful re-entering of knowledge architecture. data with each function consistently In a further recent development, in building on the model rather than September 2020, the establishment of constructing their data set. the ‘Industrial Digital Twin Association’ Future development was announced. Commenting on this, There is intense activity within Frank Melzer, a board member of Festo international associations and and head of the steering committee standards bodies to continue the work of the National Platform Industrie 4.0, of defining the structure and use of said: “We are pleased that through the digital twin. For over 20 years the VDMA and ZVEI we have been the eCl@ss e.V. organisation has been given a neutral, yet industry-oriented, working on classification standards. global platform that will carry the core Now there is a clear convergence technology of Industry 4.0 as an openbetween what was originally a crosssource into the world.” More than 20 sector data reference standard and leading digitalisation companies are the Industry 4.0 vision. This standard now working together in this alliance provides a structure that has already to enable data availability throughout been adopted by more than 4,000 product life-cycles and enhance the customers to semantically define value creation opportunities. ! even complex products and assets to support other machine-readable tools Steve Sands is head of product such as AutomationML, PLC Open, management at Festo GB.

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PROCESS CONTROL

MAKING PROCESS CONTROL MORE FLEXIBLE

For decades large-scale automation systems have been at the heart of production line control. Today, driven by changes in consumer demands, an alternative plug and produce modular automation approach is emerging, says Ralf Jeske.

M

odularisation breaks down systems, plants, processes, and unit operations into standard, modular components, much like those popular children’s building bricks that can be mixed and matched freely to make any number of different creations.

The concept centres around the prefabrication of specific and complete operational ‘packages’, which include the automation to control them. The ease of assembly this brings has led to major reductions in on-site work time, complexity and a reduction in the possibility of error.

As a concept, modular automation has been around for some time, with construction and shipping industries leading the way in early adoption.

Until recently, however, its usage was confined to just a small number of examples in process industries, with no large roll-outs.

However, thanks to advances in the automation technology that controls mechanical industrial equipment, modular automation has become easier to integrate into existing systems which means that it is now beginning to be adopted for use more widely within industrial production lines.

This is a big change from the large-scale ‘plant-wide’ automation systems that have been at the heart of production line control for decades and are designed to supervise and control entire production plants.

The aim is to modularise common unit operations into packages to allow customising of both the products made and the production quantities, giving major competitive advantages in terms of flexibility and time to market. The

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concept also allows rapid changes in deployment of production assets, to make specific product types and volumes when and where needed.

NAMUR as a catalyst Helping to move modular automation forward is work done by NAMUR, the international user association based in Germany that focuses on automation technology and digitalisation in process industries. The group was started 70 years ago to support the chemical industry, although it now covers other process industries as well.

NAMUR has led the fundamental efforts to develop standards which serve as a base for modular automation to be built upon in industrial plants. It sees increasing flexibility of production plants, using modularisation, as a key tool to meet fast-changing market demands, especially for chemicals and pharmaceuticals.

MTP as a building block

easily integrated. This is described in the standard VDI-2658, which was developed in Germany but is now being adopted as IEC 63280 for automation engineering of modular systems in the process industry.

The key benefit of using PEAs is that it takes less time and on-site work to deploy production lines and equipment. So, with the automation already integrated into the mechanical production equipment, as MTPs within the PEA, the actual deployment is relatively easy.

Flexibility to meet rapid changes in market demand has been a major driver, especially for the pharma and biopharma sectors where it is becoming common to make very small batch quantities of highly specialised products and medications.

Pilot trials A good example of what is on the horizon can be seen in an application for Bayer AG – this life sciences company successfully conducted a pilot study based on ABB-developed MTP control sub systems and a modular

Specifically, a few years ago, NAMUR introduced the Module Type Packages (MTP) standard for building modular automation capability into a Orchestration layer process module (or PEA, Process Operations Supervisory control Equipment Assembly). The PEA includes the combined mechanical equipment and controller, and uses an MTP interface, which contains Architecture network a vendor-neutral and functional description of the process module Module layer automation and can be generated by the engineering tool of the MTP MTP MTP module.

Through a simple import of the MTP into the process control engineering of the production © ABB This simplified schematic shows the modular automation plant, the module can be orchestration layer and module layer.

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Control Engineering Europe

PROCESS CONTROL

configuration tool, running with a modular-enabled ABB Ability System 800xA for the orchestration.

This is the world’s first commercial modular-enabled process automation solution, and Bayer has publicly stated that it sees it as a first step in moving from monolithic automation systems covering the complete production plant to a more flexible and marketoriented plug and produce solution.

We are therefore steadily gaining the ability to confidently build up various PEA process blocks for steps such as evaporation, separation, filtration, polymerisation, etc. We can now put these mobile building blocks together in the right order and configuration to precisely meet the market demand for whatever we want to make. It is important to note that we usually talk about rising demand, but this can be equally relevant and valuable in times of falling demand, thus allowing capacity

to be redeployed very easily and cost effectively.

The modular automation sector now is using the term ‘numbering up’ capacity instead of ‘scaling up’, reflecting the number of PEAs put into service to meet demand. The modular, flexible approach allows both regional redeployment as well as product-specific redeployment. This clearly offers much greater flexibility than would ever be possible using the large, fixed-plant infrastructure that most industrial processes are built upon today.

Instead of a big distributed control system (DCS), in the future automation will truly be distributed where needed.

And with the control built into the PEA, it is not complicated to get production started. You add an Ethernet connection, power up and run.

This concept can easily be used for new greenfield lines, with the initial focus on the pharmaceuticals, fine

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chemicals and food & beverage sectors that will see benefits in both flexibility and time-to-market.

Brownfield upgrades, in particular unit operations, are also a potential area of application. For example, if environmental laws concerning water are being tightened up in a specific country there could be a strong business case for an OEM to build a water treatment PEA module in a container, complete with MTP automation, that can simply be plugged into the process for easy and cost-efficient compliance.

Modular-enabled automation affords unprecedented agility to react quickly to market changes while keeping wastage and downtime to a minimum. It is an effective solution for a fast-moving world that needs to react in real-time to changing consumer habits and requirements. !

Ralf Jeske is global product manager for ABB Automation, Germany.

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SKILLS

BRIDGING THE AI SKILLS GAP FOR MACHINE MANUFACTURERS More knowledge is needed for effective use of artificial intelligence (AI) for machine learning (ML) applications, argues Antti Karjaluoto and Arto Peltomaa.

A

rtificial intelligence (AI) talent is difficult to find; and few industrial companies have enough in-house AI talent.

AI will transform many jobs, and companies should give every employee the knowledge they need to adapt to new AI-enhanced roles. AI resources help implement new business models and better services, but first user acceptance is required.

During the last decade, AI design, development and implementation has expanded in many sectors.

Organisations are struggling with AI business potential understanding and with finding AI talent.

A growing number of countries have recognised the opportunities provided

by AI and have prepared a national artificial intelligence strategy. In 2017, Finland was among the first countries to launch an artificial intelligence program. The objective of the program was to make Finland a leader in the application of artificial intelligence.

The Finnish Artificial Intelligence Programme identified a small portion of companies as forerunners in AI implementation; a majority of companies are at the early stages of using data and AI in operations.

Addressing the AI skills gap A way to address the AI skills gap is to increase resources for digital, math and technical education in general.

In addition, the current education system in Finland does not yet pay enough attention to applying AI in different fields. Academic and training programs are unable to keep up with the rapid pace of innovation with AI. AI education should start early and take place for every education stage.

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Academia, companies and public sector officials must work together and ensure comprehensive AI curriculums will be available. Massive open online courses (MOOCs) show the way and are a good example of a modern way to educate masses with basic AI knowledge.

However, deeper understanding often requires tailored education modules.

The manufacturing sector is currently lagging behind in AI and ML use compared to many other industries.

Adopting new technologies, especially in process industries, requires pedantic planning, which is time consuming.

Companies have long histories in optimising production, and as the life span of investments can last for decades, changes cannot be made rapidly. In addition, the safety and environmental regulations require strict governance.

Drawing from the sector estimates of the PwC AI impact index, PwC [formerly known as PricewaterhouseCoopers, a professional services firm] estimates that by 2023, individual industry sectors may increase operating margins (how much of each euro of revenues is left over after both costs of goods sold and operating expenses are considered) by 60 to 100%. The difference in the industry specific “AI boost curve” shapes reflect the impact of two factors: 1) the speed the industries are capable of adopting different AI applications and 2) the AI solution development to address the industry-specific business issues.

Benefits and barriers In manufacturing, short-term benefits are expected to mostly come from process automation and productivitybased solutions. In the mid-term, more complex processes can be automated as intelligent automation offers

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considerable potential, and predictive maintenance and optimisation applications further boost performance.

Productivity gains from AI and ML are not only dependent on the introduction of the technology itself. There also is a need to change the organisation of work and increase employees’ knowledge.

Research shows the biggest barrier to AI and machine learning adoption is the skills gap. Most of the time, surveys refer to the technical skills needed to develop AI and ML solutions. However, the biggest skill gap in AI and ML spans the organisation.

The Finnish Artificial Intelligence Programme end report pointed out that, based on its survey, Finland has high quality education for those aiming to be AI professionals (information technology, mathematics), but there is a gap in the AI applier field. In these fields, the effects of AI would be seen fastest. The working group stated that to achieve the ambitious AI targets, the most important things are to ensure versatile education will be available, investments are made in new education methods and programs are created to attract talent to Finland.

Continuous education of employees is a challenge, and different operations and mechanisms can address the concerns. A critical factor is to increase management awareness and knowledge regarding the AI opportunities, to secure enough input for new flexible education methods.

Competence requirements Employee competence requirements are affected by the changes in the work demand in the job markets.

The need for new talent is increasing Control Engineering Europe

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SKILLS

at a rapid pace in tasks where AI will be developed and applied. This demand cannot be addressed by the usual education path. New operations and mechanisms are needed to help improve existing employee skills efficiently.

Much of the employee competencies are based around on-the-job learning, so companies have more responsibility for competence development.

Companies actively seek ways to reeducate employees internally or in cooperation with other companies.

There are numerous approaches exist to educate, but little workplace learning in Industry 4.0 contexts exist.

Organizations need an adequate performance appraisal strategy and adequate workforce training with self-regulated, reflective, collaborative and blended learning to lower risk of excluding workers from Industry 4.0 environments. Organisations without adequate training risk impacts on

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production efficiency, product diversity and quality.

Companies need to equip existing professionals with the AI skills to apply their knowledge in the AI-driven world.

This is supported by a 2018 study of Future Workplace and The Learning House, which highlights that training the workforce for AI and ML skills could be the efficient way to fill the skills gap.

Letmathe & Schinner (2017) state the success of workers will depend on flexibility and problem-solving competencies as well as willingness to engage in lifelong learning; otherwise workers will be unable to keep up with the required changes in their workplaces and work procedures. This challenge also might explain why many companies are reluctant to invest in cyber-physical systems (CPS), which often includes AI. Competence management on the organisational level, as well as the reform of public education, are important factors for introducing CPS.

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Case study Free and general-level online training on AI and ML is available from major technology providers (such as IBM, Microsoft, Amazon and Google) or from MOOCs organised by prominent universities.

An example is ‘Elements of AI,’ a 6-module online course created in co-operation by the Finnish technology company Reaktor Ltd. and the University of Helsinki. Typically, the aim of this type of training is ‘to demystify AI’ to encourage a broad group of people to learn what AI is, what is it good for and what are its limitations.

Machine Learning Academy (MLA) from Dimecc Ltd. in co-operation with Futurice Ltd., is a focused and industrytailored approach for closing or at least narrowing the AI competence gap.

The first course focused on the Finnish machine manufacturing industry in fall 2018. The second course closed at the end of April 2019. This initiative was Control Engineering Europe

SKILLS

also highlighted in the final report of Finland’s National AI Programme as an innovative example of AI-related education.

MLA’s primary target audience consists of research and development (R&D) supervisors and engineers as well as business and product owners who are managing and/or participating in AI/ ML development projects. To succeed in these tasks, they need to understand how to specify, plan, evaluate and manage development or insourcing of sub-entities that contain elements of AI and ML. For example, for R&D engineers, it is important to understand how introduction of these new technologies will change the capabilities, boundaries, schedules and interfaces of their product development processes. After the course, participants will have an understanding of the fundamentals of AI and ML as well as an ability to recognise and manage development tasks that aim to benefit from use of these new methodologies.

MLA consists of seven full-day training modules with supporting pre-reading materials, hands-on exercises and homework. The training starts with high-level topics, such as reviewing typical business drivers and examples of ML applications.

In the more technical modules, ML methodologies are covered (supervised learning, unsupervised learning and deep learning), followed by data understanding and ethics of AI. The sixth module helps the participants understand how real-life AI/ML projects are executed. The last module reviews course projects.

Throughout the course various types of business and technical canvases are introduced and used as learning tools. Their main purpose is to help the participants understand where they need to focus and which stakeholders they need to engage with during the different phases of data science projects.

For example, the ‘Business Objective and Context’ canvas used in the first module directs its users to work together with business owners and those who fund Control Engineering Europe

the project when answering questions such as: ‘What is the business objective [of this project]?’ and ‘How does it fit with our business strategy?’

Cross-disciplinary project team expertise is often used in a typical data science project. Given MLA’s primary target group, it is not surprising in their feedback the participants appreciated getting more understanding on how ML projects can drive and shape actual business impacts. Also, topics related to preparing and running practical ML projects were valued, such as data preparation and comparison of different ML methods. According to one participant, “Often we do a lot of work just to see that we are stuck with insufficient data.”

During the MLA course, the participants are expected to plan and specify a real machine learning project.

The course modules are arranged in such a way that their content follows the flow of a typical ML project (see Figure). The course arrangements also provide the participants with several opportunities to discuss their projects with lecturers and the other students and share and compare their approaches.

To complete the project assignment, the participants also need to get contributions from various internal stakeholders, such as business and process owners, technology developers and product managers. As topics related to the course project are introduced and addressed throughout the course, the participants are encouraged to engage with these stakeholders and get their commitment to the new approach. The aim is by the end of the course, each participant has a project specification which key stakeholders are already familiar with and which is detailed enough for starting an in-house development project or sourcing it from an external supplier. Each participant presents a course project in the last module.

Although first MLA course participants came mainly from R&D, project topics covered a variety of

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internal functions: finance, sales, manufacturing, customer care and human resources.

Improve AI/ML learning Recommendations to help companies, academia and governments companies with AI and ML training: 1. Develop a customised curriculum for your industry. Instead of trying to compete generally in AI with leading tech companies such as Google, we recommend becoming a leading AI company in your industry sector where developing unique AI capabilities will allow you to gain a competitive advantage. How AI affects your company’s strategy will be industry, company and situationspecific.

2. Focus on educating the whole company personnel. Rather than establishing separate AI units within the organisation, we recommend that AI competencies and understanding should be increased at all levels – from the management level to the shop floor. The same principle applies on the society level.

3. AI training should encourage concrete pilots and use cases. Build AI training curriculums that encourage concrete pilots and use cases. This helps to turn AI concepts into practical value.

4. Reform existing public education.

Explore opportunities to establish an AI education voucher or an education account which would stimulate functioning adult education markets. Increase the amount of web-based training courses and open university courses for all. Integrate AI education also with vocational school curriculums.

Antti Karjaluoto is disruptive renewal officer at Arto Peltomaa is program manager; and Risto Lehtinen is head of co-creation at Dimecc Ltd., a member of Industrial Internet Consortium.

This feature originally appeared in www.controleng.com November 2020

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FIELD LEVEL COMMUNICATIONS

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O

PC UA was introduced in 2008 and is a well-established industrial communication technology for cross-vendor interoperability between devices and software applications based on a common industrial framework (IEC62541). The framework of OPC UA consists of a higher-layer protocol, information modelling capabilities and integrated security mechanisms.

OPC UA is being combined with complementary technologies and standards to deliver the overall promise of a unified, standard-based communication solution. Examples include Ethernet, Ethernet APL and Ethernet TSN (IEEE 802.3 and IEEE 802.1) as well as higher-layer protocols such as UDP/IP, TCP/IP or MQTT. In future, OPC UA will extend to the field level including the use cases “controller-to-controller”, “controllerto-device” and “device-to-device” so that consistent, unified IIoT communication solutions are available that provide end-to-end connectivity and interconnectivity from field to cloud and vice versa, covering all

requirements for industrial automation, such as real-time, functional safety and motion control (see Figure 1).

Can instruments and machines not use OPC UA and send data directly to the cloud?

Controllers from most automation manufacturers provide OPC UA connectivity to the diversity of IT and OT applications (such as SCADA, HMI, MES/ ERP systems), and even up to edge or cloud systems. However, field devices today are typically connected via field buses and therefore do not provide direct OPC UA connectivity. Gateways and/ or controllers are usually required to filter or aggregate data and make this available over OPC UA. The advantage of a direct communication/access to a field device via OPC UA is that there is no need for any conversion of the information (semantic) and/or the protocol.

In addition, most controllers today provide limited access to information that originates from the devices connected to the controller. Direct connectivity provides full access to the information with one consistent industrial communication standard and a common information modelling standard. Finally, using OPC UA as a common and consistent standard provides higher connectivity flexibility while Figure 1: The Field Level Communications (FLC) initiative is extending OPC UA to also providing cover additional use cases: Controller-to-controller (5) and Controller-to-device (6), secure access. including Device-to-device.

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November 2020

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The Foundation’s vision is to provide one consistent and standardized IioT communication solution so that devices from different vendors are able to talk to each other from the field level up to the controller level, and even up to the cloud.

Because OPC UA (IEC 62541) and Ethernet resp. Ethernet APL and Ethernet TSN (IEEE 802.3 and 802.1) are vendor-neutral international standards, users do not have to deal with incompatible protocols or proprietary systems.

Adopting the OPC UA over TSN/APL standard will allow them to benefit from multi-vendor, peer-to-peer communications and control between sensors, control devices, PLCs and distributed control systems, without needing costly and time-consuming software development, or cumbersome gateways and bridges.

How important is the differentiation of fieldbuses in a digital, integrated world?

Digitalization and new digital services require the integration of IT technologies with OT products, systems, solutions and services across value chains which stretch from design and production to maintenance. Fieldbuses are solutions for the field and are therefore used to connect field devices to controllers (Programmable Logic Control, Motion Control) or distributed control systems (DCS). This is why in a digital, integrated world a direct and standardized connectivity across devices and applications from field to cloud and vice versa is essential. In order to exchange production and processrelated information inside and outside a factory with a single IIoT communication solution. ! opcfoundation.org Control Engineering Europe

PRODUCT FORUM •

www.controlengeurope.com to read the full story

Alarm systems management

Complex industrial systems require complex control systems – but carefully thought out alarms systems EEMUA is the acknowledged leader in the field, with EEMUA 191, ‘Alarm systems - a guide to design, management and procurement’, being regarded as the benchmark in alarm systems management.

The EEMUA Alarm Systems e-learning module provides an introduction to EEMUA 191 and is positioned at the awareness level. It offers simple and practical guidance to managers, designers, supervisors and operators on how to recognise and deal with typical human-factor problems involving alarm systems. Its

scope covers many sectors, including the energy, process and utilities industries.

The e-learning is recommended

for both discipline and projectfocused engineers from a variety of backgrounds who want to gain an introduction to the fundamental principles for design, management and procurement of alarm systems.

The course is also relevant to engineers and managers from operating companies as well as specialist contractors and equipment suppliers.

Visit the EEMUA website for further details. www.eemua.org

TO BE FEATURED IN THE CEE PRODUCT FORUM Contact Adam Yates on +44 (0)7900 936909 or email Adam.Yates@imlgroup.co.uk Creating a successful control environment Know what you want, plan what you’ll get, check that you’ve got it!

The EEMUA Control Rooms e-learning module provides guidance to engineers and the wider teams involved in the design of control rooms, control desks and consoles. It will help during newbuild and modification projects, as well as evaluating existing set ups where people operate industrial processes and activities on facilities such as chemical plants, power stations and oil refineries.

The e-learning will benefit anyone with an interest in process plant control rooms and control desks using Human Machine Interfaces.

It is especially relevant to control

Control Engineering Europe

engineers, control room console (and HMI) designers and vendors, control room operators, engineering consultants, engineering contractors,

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engineering managers, facilities managers, graduate engineers, plant operations managers, process safety managers, SCADA engineers and systems support managers.

The e-learning is positioned at the awareness/introductory level and is an optional precursor to working through EEMUA 201, ‘Control rooms: A guide to their specification, design, commissioning and operation’.

Visit the EEMUA website for further details. www.eemua.org

November 2020

31

The Open Road to Autonomy The road to industrial autonomy is open and variable, offering myriad choices for operational excellence.

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