Control system integrators: How to excel with automation upgrades, part 4
Automation system integrators answer additional questions and reflect on audience poll and related research.
- See how an edited transcript from a popular Control Engineering webcast provides automation system integration advice about technologies of interest as noted in a poll and research.
- Explore control system integrators’ answers to audience questions about control system integration projects on edge computing, AI/ML applications, PLC application integration and MES.
Control system integration insights
Edited transcript from a popular Control Engineering webcast provides automation system integration advice about technologies of interest as noted in a poll and research.
Control system integrators answer audience questions about control system integration projects on edge computing, AI/ML applications, PLC application integration and MES.
Control system integrators answered webcast registrant questions and reflected on an audience poll and research about automation technologies that can help with automation system integration projects. The advice, part 4 in an article series, was adapted from a transcript of popular Control Engineering webcast, “Automation Series: How next-generation automation will help in 2023,” with help from OpenAI’s ChapGPT, then reviewed and further edited by webcast moderator, Mark T. Hoske.
Webcast instructors were:
- Tyler Graham, director business development-digital transformation, and Randy Rausch, director of technology-digital transformation, Eosys
- Mike Howard, vice president of system integration at George T. Hall
- Matt Lueger, executive vice president, NorthWind Technical Services.
What technologies help with automation selection, design, integration, implementation and use?
A poll question during the live webcast asked registrants about technology interests to help the system integrators to better shape their presentations. The question asked the audience to indicate the two most important topic categories to help in your job in the coming year. Below see the choices and percentage respondents.
27% Control systems and related strategies, including controllers, edge and cloud computing, machine control, AI/ML and mobile devices.
27% Networking and information, including I/O systems, HMI, SCADA, analytics, safety, cybersecurity, Industry 4.0 and IIoT.
26% System integration, workforce development, competitiveness and applying automation.
20% Control equipment and energy, including actuators (motors, drive, motion controls, pneumatics, hydraulics), sensors, power protection and distribution and energy efficiency.
Prior Control Engineering audience research asked a similar question (check all that apply). Results follow.
59% Control systems and strategy
34% Automation networking and information.
46% Control equipment and energy.
43% Automation industry, system integration and workforce development.
Two system integrators reflected on the results.
Howard: I notice how automation industry system integration is heavily favored. I think as technologies get more complex, people will need to reach out to those that have that expertise to help navigate through those technologies.
Graham: I found it interesting that the online poll seemed to have a heavier weighting on networking.
Audience questions from webcast on next-generation automation start with edge computing
Question and answers topics from the audience included edge computing, artificial intelligence and machine learning (AI/ML) applications, programmable logic controller (PLC) application advice and manufacturing execution system (MES) objections.
Hoske: How is edge computing fitting in with automation, controls and instrumentation?
Rausch: Edge computing is a broad term and question. Edge is usually meant to counterpoint or contrast cloud, but as computing becomes more available, you don’t have to do everything in cloud. A lot of people in our space are scared of the cloud, but really these implementations can work very well. You can put computing devices closer to machines and the edge and share the information among them.
Data has something called gravity. The more data you have somewhere, send your processing to that area instead of pushing all the data somewhere else. Edge is an important part of how you would do an overall processing and optimization design and for understanding your operation. There are lots of ways to configure edge computing. It’s useful. I’m a big fan.
Howard: I agree. Edge computing is a big topic. It’s easy to say “edge,” but what does that mean across the board with different technologies and manufacturers?
Lueger: We’ve seen people looking for edge solutions when they may not be ready to invest in a centralized distribution model. Adding a couple of edge applications to get started, and plan for the future is a good step down that path.
AI/ML applications to help automation, controls
Hoske: What are AI and ML applications to help automation and controls?
Rausch: AI/ML has a lot of examples in industry today. A lot of predictive maintenance applications that forecast remaining useful IO equipment. AI/ML is great for that. Used a lot of times in quality checks, certainly around visual inspections. It’s a great tool for supply chain management to do your demand forecast accuracy, predict raw material prices, do inventory management, software performance improvements, lots of examples of all of those types of things.
I would caution you if you have a hard problem, don’t just say AI/ML will solve all the problems. You have to formulate the problem well. AI/ML is great one if you’re having trouble thinking through all dimensions and can’t quite find what you’re looking for. If you have the data available, AI/ML can provide great applications and tools.
Howard: I recently sat in on a presentation for a vision application that incorporated AI, and we’re touting the fact that it’s more powerful than standard vision systems in that you don’t have to teach the system as much as you would with the standard vision.
Graham: There are a lot of applications for AI/ML. I think the most common applications are ones that are more repeatable, high dollar assets. It becomes increasingly difficult to apply AI/ML to individual one-off manufacturing problems. It certainly can be done, but I think as you know heard earlier, the data isn’t always there. It’s kind of assumed, “Hey, we have all this data.” Even when you have tons and tons of data, it still may not be the right ones required to apply to AI/ML. So back to defining the problem and really understanding it is key to make sure that you make good use of AI/ML.
Lueger: If you have the right set of data, it’s surprising how fast you can get results. But oftentimes, turning that into the right results that can be actionable is what takes the longest time in that process.
Integration with existing PLC applications
Hoske: Do you have advice about integration with existing PLC applications?
Lueger: The amount of documentation that you get with existing PLC applications can vary wildly. And success for that project is tied to the amount of information or documentation that comes with it. So spending some time up front to understand what’s there and what documentation needs to be added and understanding that there might be a big leap in adding documentation if it doesn’t exist, it’s not as easy as just jumping in and making the integration. Make sure you have time for project commissioning, and extra time if there isn’t much documentation. As much as you think you’ve learned about a project, it’s easy to find gaps.
Howard: The older the equipment, the harder it becomes. Right?
Lueger: That’s right.
Help overcoming MES implementation objections
Hoske: What’s the most common argument against implementing an MES system with a customer?
Graham: Not knowing MES capabilities is often why MES isn’t used. Arguments against MES are there are multiple ways of solving problems. Customers need to clearly define what the problems are. I think MES and manufacturing operations management (MOM) applications are still very alive and well today. I think MES can be part of a digital transformation solution and very foundational to doing a lot of additional analytics. There are a lot of times where a full-blown MES is not required. Plenty of other tools can perform data collection and visualization and still solve problems.
A certain agility is needed to get to an MES. A lot of MES software is perceived as monolithic and a humongous undertaking. A phased approach can help. Step through and find the most value first and then build out. That’s why having that long-term view is important in addition to short term. There are arguments that other, simpler, more agile tools than MES can work for certain applications.
Lueger: People are afraid of the beast they think MES is when they start to talk about it, and that keeps them from getting into it.
Mark T. Hoske is content manager, Control Engineering, CFE Media and Technology, firstname.lastname@example.org.
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Webcast instructors answer more audience questions about next generation automation in this article.