IIoT_20_05

MAY 2020

Inside: The way forward p2 Analytics best practices p6

Supplement to Periodicals Publication

PRODUCTIVITY AND BEST PRACTICES: EDITOR’S COLUMN Kevin Parker Editor

The way we live now

It’s estimated one-third of workers are logging in from home

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The briefing, which took place on April 8th, was hosted he technology infrastructure that’s served us by the Manufacturing Advocacy and Growth Network so well the last 75 years is a product of the (MAGNET), a consultancy that supports manufacturers in crisis of World War II and its aftermath. north-east Ohio.

Isn’t that what they say? “Never let a good crisis go Swagelok in recent years ran a table-top exercise to to waste!” Engineers came home from the war and gauge the impact of a possible the interstate highway system and pandemic. Swagelok’s use of key the automotive industry, television documents to define concepts and and radio made life in the suburbs Digital solutions outline policies gave guidance to possible. the attending companies on how This technology infrastructure, and will address biological to get started in terms of employee the social and political consensus challenges. self-assessments and rules for social it supported, is aging rapidly. How distancing in plant environments. profound an impact will the CoronaStories abound about the use of virus crisis have on the technology 3D printers to make protective protection equipment. infrastructure?

It’s all good stuff but until 3D printing technology can be “Companies will accelerate their digital transformascaled for mass manufacturing its impact will be limited. tions,” said one guest on MSNBC’s Morning Joe. However, one recent story went further.

After all, who would want to invest capital in industries dependent on labor and used to single-digit profit margins, such as restaurants? How can airlines or restau- Beyond 3D printing rants be profitable if forced by social distancing rules to The Bird Mark 7 ventilator was first introduced in operate at 50% capacity?

1957 by Dr. Forrest M. Bird, an inventor and biomedical Apple and Google are working together to make autoengineer best known for having created some of the mated contact tracing possible. With contact tracing, our first reliable mass-produced mechanical ventilators for smart phones will alert us if we’ve been within six feet acute and chronic cardiopulmonary care. of contact with someone who has tested positive for the The Mark 7 was a long-time fixture of the 1960s virus. Just think of the implications. hospital scene. It was a reliable, safe and effective It’s estimated one-third of workers are working from application for patients with respiratory problems. It home now. was manufactured well into the 1980s and can still be In the 1990s, editors did their jobs equipped with a telefound in hospitals around the world. phone and word processor. Today it’s a smart phone, emailThe National Strategic Research Institute (NSRI), UC equipped tablets and Zoom calls that structure their day.

Davis and Livermore Instruments have partnered to rapidly redesign the Mark 7 to include simplified supply chain components and to mass produce a modern verAll manufacturing is essential sion that adds critical features.

CFE Media & Technology editors have focused coverDavid Fergenson, CEO of mass spectrometer manufacage on the story lines of interest to the engineering turer Livermore Instruments, Oakland, CA, said the goal is to communities. help meet global demand for ventilators through an openMore than 200 manufacturers attended a Zoom call source specification for production of the updated Mark 7. briefing from Swagelok Co., the maker of fluid system Read more about these stories and others on the Plant components, that outlined steps the company is taking to Engineering website and newsletters. IIoT continue operations amidst the Coronavirus crisis.

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MAY 2020

Industrial Internet of Things

www.controleng.com/IIoT

THE AGE OF ANALYTICS

Introduce analytics best practices into industrial environments Put analytics in the hands of the process experts who understand the data best

By Edwin van Dijk

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hat is the best way for a company to collect and analyze the data needed to make data-driven decisions that support good business outcomes? Let’s look at some of the best ways to successfully implement advanced analytics for multiple operational stakeholders.

Many companies that monitor and control production processes store years of sensor-generated time-series data in an historian. More and more sensors are today used to monitor or predict asset performance. Besides sensor-generated time-series data, operational data is gathered in many forms, formats and systems, including batch records, product quality data, shift logbook and maintenance data.

If made accessible and ready to use for people who understand what the data means, it can improve operational performance.

Industrial analytics landscape In addition to real-time process data, information gathering encompasses things like feed stocks and customer and supplier information, as well as other business process data.

This data also can be analyzed to improve performance. Data scientists have used tools such as R, Python, Jupiter and others to do this work.

Data is captured from various sources, pre-processed and prepared for use in data models. www.controleng.com/IIoT

FIGURE 1. Analytics achieved through use of data models, the realm of data scientists. All figures courtesy: Trendminer

Subject-matter experts help data scientists better understand the data’s significance, contribute to building the data model and validate its use prior to deployment. These projects take time and resources.

In recent years many analytics tools and applications have been introduced. The variety of data, data sources and objectives create confusion as to which tools to use for which data, by whom and for what?

For businesses, tools are available to design, measure and analyze business processes, customer behaviors and supply chain performance. Other tools are used to analyze, monitor and predict operational performance. For production processes available applications can be mapped across two axis: analytics maturity (horizontal) and fit for purpose (vertical).

In this analytics landscape (see Figure 3), we see generic tools or platforms

to track operational performance (descriptive analytics), such as business intelligence software (BI tools) that retrieve, analyze, transform and report data. Tools and platforms are available for all sorts of generic big data, even for higher analytics maturity levels. In many cases these tools and platforms require customization by data scientists for specific use cases.

For more specific data, such as sensor-generated time-series data of the production process, we see the various historians in the upper left corner of the figure. Historian vendors also deliver some analytics capabilities, but often require data engineers or scientists to work with analytics capabilities to bring the results towards higher analytics maturity levels such as discovery, diagnostic or predictive analytics.

Finally, another set of applications includes tools to empower subject matter experts, such as process and asset engineers, who already understand what the data means, to analyze the data themselves. We call those tools self-service industrial analytics tools.

Empowering operational teams State-of-the-art self-service analytics applications makes it possible for non-data scientists to analyze their production processes. The solution should enable the team to analyze, monitor and predict production performance within its operational IIoT For Engineers

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THE AGE OF ANALYTICS

FIGURE 2. Key capabilities of a state-of-the-art self-service analytics solution.

context, and at the same time give each stakeholder a personalized production cockpit to monitor operational performance related to the key performance indicators (KPIs) they are responsible for.

To be effective, we believe a selfservice industrial analytics solution must be a fully web-based plug & play solution delivering value out of the box, either installed on premises or provided as a full SaaS solution.

It should support a wide variety of historians and allows incorporation of non-industrial contextual data in analysis (e.g., financial database, maintenance management systems, laboratory information management system (LIMS), out-of-expectation (OOE) results, batch systems, unstructured data and others).

Process and asset engineers must be able to use high speed trend analysis to quickly search and validate production issues, based on embedded artificial intelligence machine learning (AI/ML) capabilities. They must be able to quickly find root causes for process anomalies through pattern recognition, statistical capabilities or provided recommendations, without the need for building data models.

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IIoT For Engineers

On the other hand, engineers should be able to convert multiple periods of good process behavior into fingerprints to use in monitoring operational performance. In case of deviations they can determine who to send notifications to for taking appropriate measures. When physical sensors are not available, soft sensors can be created to analyze and monitor product quality.

The solution should also allow for predictive performance scenarios, global collaboration, knowledge capturing and creation of personal production cockpits. In this way each operational team member can make better decisions, faster, and derive benefits related to their organizational role.

Introduction to the organization A proven approach to success with industrial analytics is to start small and scale fast. By selecting some practical use cases based on timeseries data the value of an analytics tool can be proved in three main areas: • Solve previously unsolved process performance issues

• Verify hypotheses and prove them to be either true or false, so they can be addressed or ruled out for the future • Find new ways to improve performance, with the data providing new insights.

Some successful use cases that led to corporate rollouts for users leveraging self-service industrial analytics: 1. Reduce emissions by improving off-gas treatment: Search and discovery analytics proved the engineer’s hypotheses, enabling them to reduce problematic situations with off-gas treatment by 63% and greatly reducing emissions.

2. Production quality optimization: By comparing good quality periods with bad quality periods in production runs and use of “layer compare,” engineers easily identify what caused a quality problem.

3. Eliminate a potential production loss of 125 tons: Process engineers at a chemical facility were experiencing unwanted production www.controleng.com/IIoT

stops. By tagging good batches as “fingerprints” they were soon able to identify deviations in production runs – leading them to a problem in the control system. Finding the root cause with fingerprints helped them eliminate a potential production loss of 125 tons, or almost $300,000.

4. Energy monitoring without Excel: Energy monitoring is an important factor when addressing sustainability. In the past, one chemical company would use Excel files to manually compare energy consumption data from one year to another. Self-service analytics now allows the engineers to easily bring a certain year into focus.

By adding the following years of energy consumption as layers, they are able to easily compare large periods of time — without needing to use Excel.

5. Other typical cases self-service analytics can be applied to: • Yield increase through cycle time reduction • Predict fouling for heat exchangers • Monitor pump performance in a distribution network.

Once it’s proven analytics in the hands of the operation personnel brings success, it can be introduced into the larger organization. With the technology and its methods proven within the smaller group, management can support wider implementations. A team must be created to introduce the tool to additional potential users and win their support.

To successfully introduce the new analytics application its value must www.controleng.com/IIoT

FIGURE 3. Topics to address for successful implementation of new tools and methods.

be demonstrated to each user. When they see quick results in support of their own work, they are more receptive to change. It helps to have local champions who are thoroughly trained and can help others in case of questions on how to best address specific use cases. Internal communication about the roll out and the successes the users have, helps accelerate the introduction.

Organizational evolution On an organizational level, the following factors must be put in place to make the introduction of state-ofthe-art industrial analytics software a success: • Create a dedicated roll-out team within the business unit to run the project in cooperation with local teams • Create a project management team with engineering and production data analytics expertise.

These experts can help the local plants and the users to become successful with the new tool • Create an asset analytics community to exchange ideas and best practices

• Implement individual success plans, in collaboration with managers of the business units, to help users change behaviors and feel supported • Set up a digital working group to act as a steering committee, with stakeholders from business unit production management, to help smooth the introduction and global roll-out.

Concluding thoughts Becoming fully digitalized is a big undertaking, especially for brownfield operations. It’s best to start small in an area of the company where the chances of success are highest. The experience of a wide range of users implementing self-service analytics indicates their use of sensor-generated time-series data leads to a high success rate.

Analytics empowered process experts can make data-driven decisions to improve overall operational performance, meet short-term and long-term organizational goals and transform organizations for sustained market competitiveness. IIoT Edwin van Dijk is a company vice president with Trendminer.

IIoT For Engineers

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PROCESS CONTROL

Data flow is no longer hierarchical Can industrial edge computing fit into the Purdue model?

By Vatsal Shah

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ince its introduction in 1992, the Purdue model has remained virtually unchanged. Considering the blazing speed of technological change characteristic of today’s modern business landscape, is it time to re-evaluate the model’s relevancy?

When the Purdue Model for Control Hierarchy was published by Theodore J. Williams and the Industry-Purdue University Consortium for Computer Integrated Manufacturing, it quickly became the de-facto standard for how manufacturing teams thought about, architected, and implemented industrial control systems. The Purdue model became the barometer of what good

manufacturing looks like, the reference point for conversations about systems and data flows and the defining snapshot of where operational and plant floor applications sit relative to the rest of the business. In short, it defined the landscape.

With the advent of the Industrial Internet of Things (IIoT), the Purdue model may be starting to show its age. Most notably, rapid acceleration of the number of disparate connected devices and mass democratization of computing power introduces new requirements not addressed within the linear hierarchy of the model in its current form.

What is the Purdue model?

As shown in Figure 1, the Purdue

FIGURE 1: The Purdue model of computer integrated manufacturing. Figures courtesy: Litmus Automation

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IIoT For Engineers

model is a guideline for industrial automation and control systems, including network and security requirements. The Purdue model also inspired ISA-95, an international standard from the International Society of Automation that defines interfaces between enterprise and control systems.

The model, in the shape of a pyramid, represents how information flows from the shop floor upwards into high-level enterprise systems.

The model separates enterprise and operational domains into different zones isolated with an industrialized Demilitarized Zone, or DMZ, in between. Built-in security prevents security breaches between Level 0 and Level 5.

The model keeps computing and networks deterministic, i.e., ensuring that networks on the shop floor remain dedicated to the control systems and do not become “flooded” with non-production related data that could result in network capacity issues that could stop the manufacturing process.

The Purdue model also serves as a blueprint for IT systems to acquire shop floor data via the DMZ without compromising production or allowing capture of plant floor mechanical equipment for nefarious purposes.

Cyber security concerns were also addressed by firewalls placed between industrial and enterprise zones, isolating data within the zones absent explicit data sharing rules. www.controleng.com/IIoT

What are the limitations?

The Purdue model fit the world of 1992 nicely. Cloud computing was just a dream. The bulk of compute capability to run the facility and manufacturing processes was found on-premises. Data sharing between manufacturing facilities and central offices was limited to order placement and fulfillment.

These layers and zones contributed to a controlled flow of data, mostly originating from the bottom of the Purdue pyramid upwards or planning data pushed down into the model for consumption at lower levels.

The model dictated that data be organized to be hierarchical and purpose driven. Data required to run processes came into the system top down and was processed and consumed as needed at each level. Data generated from the shop floor was sent back up, sometimes being used at the Level 3 industrial security zone, but more often being passed up through the DMZ into the enterprise security zone where it was used for basic historical reporting purposes.

Today’s data flow is no longer hierarchical. Manufacturers added intelligence at the sensors (Level 1), controllers (Level 2), and “edge,” which can be anywhere along Level 1 to 3 based on where the edge device is placed. All of this is to say that points of exposure are occurring much further down the pyramid than the Purdue model ever considered.

Due to the expanded power of edge computing devices, large amounts of data can be collected at Level 1, processed and be sent directly to the cloud. IIoT, in all its interconnected glory, has demanded that we change the paradigm from that of a pyramid to that of a pomegranate.

Critics say Industry 4.0 has made the Purdue model at best outdated and at worst obsolete. These outdated applicawww.controleng.com/IIoT

LEVEL 0

LEVEL 1

LEVEL 2

INDUSTRIAL EDGE COMPUTING PLATFORM

LEVEL 5

LEVEL 4

LEVEL 3

FIGURE 2: The Purdue model with an industrial edge computing platform.

tions of the model are seen in use cases where sensor data is being collected at Level 0 and is required to be sent to the cloud to enable predictive maintenance capabilities. Sending Level 0 data to Level 5 directly violates the segmentation aspects of the Purdue model.

Stay or go?

Scrapping the Purdue model, however, doesn’t work either. The Purdue model still serves the segmentation requirements for both wireless and wired networks and protects the operational technology (OT) network from unwarranted traffic and exploits.

What is needed is a hybrid solution that integrates into the Purdue model to maintain segmentation for traditional instances of IT and OT data flow, but also provides the flexibility needed as Industrial IoT use cases become more prevalent.

This level of IIoT flexibility can be attained by adding an industrial edge computing platform software layer. With this layer, an Industrial IoT project can adhere to each level in the Purdue model. This platform layer can sit either at Level 2 or Level 3 and provide data collection capability from OT devices at Level 0, 1, 2 and 3, while also facilitating data collection from IT layers at Levels 4 and 5. The benefit is that the traditional hierarchies inherent in the Purdue model can be

bypassed where needed (i.e. sensors sending data from Level 0 to Level 5) by piping the data through the platform to ensure control and security.

Consider Figure 2, which demonstrates how an industrial edge computing platform can be inserted into the Purdue model.

The industrial edge computing platform sits inside the Purdue model, facilitating communications between any level as required. It is the data quarterback. It is the orchestration platform that makes it easy for systems to communicate amongst themselves.

Around the outside of the diagram, the traditional and established data flows will continue to persist as per the Purdue model.

The Purdue model has benefits still valuable in today’s manufacturing environment. Implementing an industrial edge computing platform into the model preserves the integrity of the system. IIoT Vatsal Shah leads the management and engineering team as co-founder and CEO of Litmus Automation.

Vatsal earned his master’s degree in global entrepreneurship from Em-Lyon (France), Zhejiang University (China) and Purdue University (USA) jointly and his bachelor’s degree in electronics engineering from Nirma University in India.

IIoT For Engineers

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