A process of evolution: creating an adaptive future for the industrial workforce

As industrial automation systems grow more sophisticated, technology is being called upon to bridge the gap between more experienced workers and the new generation of digital natives.

One of the most pressing challenges facing process industries today is the skills shortage. Every year, more seasoned professionals are retiring from their jobs, taking with them decades of invaluable experience and know-how. Meanwhile, a new generation of digital natives are being recruited to replace them. Without wishing to unfairly generalise, it’s safe to say this new generation is more tech savvy and digitally connected than their predecessors. While these skills provide unique advantages, there’s no substitute for decades of real-life plant floor experience when it comes to understanding the nuances of industrial process control.

For example, by just looking at the process from end to end, or even listening to a piece of equipment, an experienced engineer may know exactly what attention it needs, as well as what can be safely ignored. Once this vital and often highly specific application knowledge is lost, it is very difficult to get it back.

Today, innovations in control systems are emerging that can help ensure that data is turned into actionable insights, while also giving today’s workforce the tools they need to confidently grapple with complex process automation and control challenges.

Navigating increasing complexity

Meanwhile, industrial automation systems are becoming more and more sophisticated. Sensors, and the data they generate, can provide real-time insight into the performance, condition and maintenance needs of process applications at a component level. The challenge that remains is how to turn this data into actionable insights. An experienced engineer can see an alert or indicator and intuitively know what is likely to happen next. Perhaps the process can be run for another couple of months without the need for any intervention. Or it may be that the effected component needs to be immediately taken offline and maintained. Making these calls correctly requires a deep, learned, and intuitive understanding not just of the individual process, but also the subsequent impact on dependent and adjacent processes, and the bigger picture of the production facility.

Having vast amounts of data flying around the plant at any given time creates other challenges; namely, making sense of all that data, turning it into relevant, understandable information, and delivering it to the right person or team in a timely manner. In a modern plant many of the connected devices are generating vast quantities of data that was unavailable and unseen just 10 years ago. Take an electric motor, for example. Previously, the data available for that motor’s operation would be limited to whether it was on or off, and what speed it was running at. Today, sensors fitted to a motor can tell you how much it is vibrating, its RPM, temperature, load and power consumption, run hours, and other health parameters. With a fleet of motors, and other equipment to monitor, staying on top of all this data has become increasingly difficult.

To that end, the data needs to be manageable. Digital natives, who are used to seamless and intuitive user experiences in consumer electronics, expect this information to be meaningful, and at their fingertips. They don’t want to have to go to a different console or asset management system or open a spreadsheet to access it. The challenge for automation manufacturers is to make sure that relevant information reaches the right personnel, without overloading them with noise.

The evolution of automation

Cloud and edge computing are increasingly extending and redefining the role of the automation system. This evolution creates an environment that resonates with today’s tech-savvy engineering talent. In older automation systems, all process data resided within the core control layer. This limited who could access it, restricted flexibility, and slowed innovation because of the risks involved in modifying a centralised system. Modern edge and cloud technologies remove these barriers, enabling separation of concerns and making data freely accessible for faster, safer improvements.

Today, edge computing is creating a new space that sits outside the traditional boundaries of core process control: let’s call it the ‘digital habitat’. Within the core process control system sit familiar elements including field devices, controllers, I/O, HMI software and engineering software. Process data leaving this core enters the digital habitat, where it’s consumed by apps — hosted either in the cloud or locally at the network’s edge via an edge device that can be used to drive plant monitoring and optimisation (M&O) applications. These applications process the vast amount of input data and present it in a format that is meaningful to each person’s role and function. Crucially, the architecture ensures that core process control is isolated from this app level, eliminating risks that could affect the ability of the DCS to perform its key process control, safety, monitoring and communications functions.

With data and apps formally isolated from core process control, information in this new environment is freely available to a wide array of users without causing interference to primary DCS functions and endangering plant operations. The new environment provides a secure, governed data pathway to connect, collect and store contextualised process data. This can be leveraged by M&O and other analytic applications, leveraging tools like AI/ML to unlock actionable insights — leading to improved operational performance, extended asset life, better decision-making and expanded serviceability.

Modern technology can spot subtle patterns in massive datasets and use those insights to inform intelligent decision-making. Yet in an industrial process plant, raw computing power often struggles to match the hard-won intuition of a veteran operator with decades of experience. For example, a gradual change in a sensor reading on rotating equipment — like a pump or compressor — might go unnoticed by most. But an experienced engineer may recognise it as a sign of an unrelated issue that may soon cause costly, disruptive problems.

A digital environment helps capture and share that invaluable expertise. By sending process data from core control systems to the cloud or an edge platform, AI-powered monitors can analyse it and send an alert when something’s not right. Preserving and passing on know-how is essential, and digital tools make it accessible for the next generation.

Digital tools make knowledge accessible to the new generation of operators and technicians. Image: Supplied

The augmented workforce

For the workforce of tomorrow, these developments open new possibilities for effective plant management. While most problems will still require some degree of human intervention, the process of identifying them can at least be automated, saving time and resources and enabling early intervention. As the ‘digital habitat’ expands, engineers can now increasingly use smart devices to receive ‘anytime, anywhere’ updates. Real-time data on process and plant status from the DCS can be delivered to these devices as actionable information, empowering engineers to resolve potential issues faster, reducing the safety, cost and downtime implications of unresolved problems being left to escalate.

The aim of edge-based M&O applications is to enable engineers to view a range of plant data and alarms via a standard web browser using intuitive and secure dashboards to provide a consistent and easy-to-use interface that augments the ability of workers to ensure safe and efficient plant performance. As these technologies evolve further, both engineers and technicians will be able to operate at a higher level of autonomy by focusing more on value-added activities, and less on tedious tasks. New asset start-ups will also be able to be achieved more quickly and, with the increased automation of engineering, more smoothly with less risk of error.

Summary

The digital habitat of the edge environment can solve multiple problems at once. First, it provides a mechanism for capturing continuous application data. The apps within the edge environment can monitor process data for any anomalies and identify items that need attention. This saves time by reducing the need for manual inspection of equipment, while also serving to retain and store vital process knowledge. Over time, as the historical data set enlarges, it will help operators to know exactly what is going on in their systems at all times, allowing them to prioritise work accordingly.

Crucially, the edge applications need to be designed to be seamless and intuitive. Should the system identify that a device or process needs attention, a maintenance engineer with a toolbox may still need to go and inspect it, but they will have a better understanding of the urgency. In addition, edge computing enables engineers to detect or diagnose anomalies that would have previously gone unnoticed. While sensors and monitoring systems can provide granular data into an asset’s health and maintenance needs, this data can be overwhelming to less experienced personnel. No longer can the experience of a seasoned engineer be counted on to distinguish what requires immediate attention and what can be left for another day. The edge environment, and the apps that reside within it, aim to preserve that experience, by providing meaningful insights to a new generation of engineers, when and where they are needed.

Shifting non-core functions to a flexible, secure digital environment supports greater agility and adaptability while equipping the digital native workforce with the technology and knowledge required to maintain tomorrow’s productivity.

*Kim Fenrich is currently Global Product Marketing Manager for ABB and has over 30 years of diverse automation industry experience, across a wide variety of industries including power generation, processing and manufacturing. Kim holds a Bachelor of Science in Electrical Engineering from the University of Akron, and currently lives near Wilmington, North Carolina.