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| Hear the latest: CTL+ALT+MFG podcast, digital transformation experts… hosted by Gary Cohen and Stephanie NeilCtrl+Alt+Mfg Ep. 17: Rethinking Industrial Data, with Gary Tillery
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AI support for automation tasks, Reduce custom gear lead times, CNC platform, HMI update, Easier edge integration, Compact multi-axis control has safety, Ethernet switch, IIoT platform, Power platform for enclosure design, Geometric deep learning, CNC control, 8k TDI camera, Cooling motor platform 2026 Control Engineering Product of the Year winners announced
39 | Back to Basics: Inside Ethernet-APL, cybersecurity in the digital age
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How to integrate new HMI or SCADA with existing automation www.controleng.com/webcasts Ynow2026 – Yokogawa, Sept. 1-3, New Orleans www.yokogawa.com/ynow2026
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Ep. 17: Rethinking Industrial Data, with Gary Tillery of Skkynet
Live from CSIA, Skkynet CEO Gary Tillery discusses the vibe shift in industrial automation, the hidden tensions in vendor-integrator partnerships and why failing fast remains a superpower for technology providers in a world of committee-driven bureaucracy.
At the 2026 Control System Integrators Association (CSIA) Conference in Baltimore, discussions around industrial data, cybersecurity and digital transformation were impossible to avoid. Among the voices contributing to those conversations was Gary Tillery, CEO of Skkynet, who joined the Ctrl+Alt+Mfg podcast to discuss the realities of moving industrial data securely, supporting system integrators and navigating the industry's evolving architectural debates.
One of the clearest examples came when discussing cloud computing and edge architectures, he said, “The operator can't wait for the latency of the cloud and the analytics and the insights that it could get. So you have to operate at the edge.”
That perspective reflects a growing shift within industrial automation, where organizations increasingly recognize that cloud analytics and enterprise visibility must be balanced with real-time operational requirements.
NASA, connectivity
Tillery's interest in technology began early. Growing up in Houston during the Apollo era, he was surrounded by the engineering culture that defined NASA's lunar missions.
“We called us NASA brats,” Tillery said. “So I was born into technology. That's kind of what I tell people.”
That early exposure eventually led him into industrial automation, beginning with work for a system integrator,
Ctrl+Alt+Mfg podcast, Episode 17 with Gary Tillery of Skkynet.
Speakers: Gary Tillery of Skkynet
which continues to influence how he views the role of system integrators today. While industrial software vendors often focus on platforms and technologies, Tillery argues that system integrators remain foundational because they combine technical expertise with customer trust.
“They're the foundation of this industry,” he said.
System integrators are essential
Throughout the interview, Tillery repeatedly returned to the importance of system integrators (SIs) in helping manufacturers implement and sustain technology initiatives. According to Tillery, technical competency alone is no longer enough. The most successful SIs combine engineering expertise with strategic thinking and business discipline.
“We also look at from the standpoint of a perfect one to be more of a progressive living in the future a little bit more,” he said. “Not just the day to day of the quarter but looking at the industry as a whole.”
He also emphasized that today's integrators perform far more sophisticated work than earlier generations.
“They're not just wrench turners, screw drive turners, and things,” he said. “They’re developers, they're engineers, they're architects.”
This evolution is occurring as manufacturers increasingly seek partners who
can navigate cybersecurity requirements, data integration challenges and enterprise-scale digital transformation projects.
Channel conflict,
collaboration
Channel conflict discussions frequently surface between vendors and system integrators. Integrators often worry that vendors will bypass them after gaining direct access to customers. Tillery acknowledged the challenge and described it as an issue Skkynet actively works to manage.
“We spend a lot of time on this because we don't want the conflict because conflict then ruins the relationship,” he said.
Rather than relying on rigid rules, Skkynet evaluates opportunities based on customer relationships, project ownership and business realities. “We kind of look at who leads, who brought the project or the customer in,” Tillery said. While some situations require direct engagement with enterprise customers, Tillery said partner participation remains important. This approach reflects a broader industry challenge as software providers seek growth while preserving the trusted relationships that integrators have developed over years. ce
Gary Cohen is senior editor, Control Engineering, Arrowfly, gcohen@arrowfly.com.
controleng.com/podcast
Tillery provides more on cybersecurity, gridging the OT-IT divide, rethinking the unified namespace, AI industrial operations and agility as a competitive advantage at https://www.controleng. com/podcast/ctrlaltmfg-ep-17-rethinking-industrialdata-with-gary-tillery-of-skkynet
5 industrial AI trends
Future industrial artificial intelligence (AI) will be a comprehensive integration of data, algorithms, hardware and industry knowledge.
As global manufacturing accelerates its shift from the Industrial Internet to the Industrial Intelligence Internet, AI has transcended its role as an auxiliary tool and evolved into the “brain” and “nervous system” of industrial production systems. Challenges such as data silos, implementation difficulties, unexplainable algorithms and persistently high application costs remain widespread pain points constraining the large-scale adoption of industrial AI. On June 3, the first 2026 Industrial AI Summit was held in Shanghai. Dozens of enterprises and institutions shared cutting-edge practices on data governance, industrial agents, platform architecture, hardware infrastructure and scenario-based deployment, outlining the development landscape and future technological evolution of industrial AI. Five core technological trends have emerged.
Systematic, comprehensive data governance: Solidify industrial AI
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KEYWORDS: Industrial AI, digital transformation
CONSIDER THIS
Are you applying industrial AI as effectively as your competitors? When will you know?
ONLINE
See a prior article by Stone Shi, Control Engineering
China: How industrial AI deepens sustainability in 3 ways https://www. controleng.com/howindustrial-ai-deepenssustainability-in-3-ways
Data constitutes the core production factor of industrial AI, yet fragmented, isolated heterogeneous data remains the primary barrier to technological deployment. The summit reached a broad industry consensus that engineered, standardized full-domain data governance is imperative.
Rise of industrial AI agents: Reconstructing full-process operation models
Industrial agents emerged as the most discussed technological track at the summit, marking a pivotal shift of industrial AI from isolated functional tools to system-level intelligence enabling autonomous cross-process collaboration. Multiple enterprises launched targeted agent products covering full scenarios including automation engineering, production control, R&D design and supply chain management. Industry experts widely predict that a cross-domain interconnected ecosystem of indus-
FIGURE: The first Industrial AI Summit was held in Shanghai on June 3, 2026. Dozens of leading enterprises and authoritative institutions participated including PTC, Siemens, Phoenix Contact, Supcon and Emerson. Courtesy: Control Engineering China
trial agents will take shape in the coming years, enabling continuous self-learning of AI through closed-loop feedback mechanisms.
Accelerated iteration of industrial large language models
General large language models struggle to meet the rigorous working condition requirements of industry. Vertical domain-specific large models have become the mainstream of R&D, with companies in different tracks creating differentiated model products based on business characteristics.
Deep integration of IT and OT: Open control platforms
Traditional industrial control architectures struggle to be compatible with AI technology, and the barrier between information technology (IT) and operational technology (OT) severely restricts AI from penetrating the production floor. At this conference, open control platforms featuring software-hardware integration and multi-language compatibility became the key to breaking the deadlock.
Return to rational application concepts: Autonomous closed-loop operation
As the hype around Industrial AI climbs, the industry has begun to discard gimmicks like “unmanned factories” and “fully black screens.” Rational implementation and value prioritization have become the consensus.
Future industrial AI will be an integration of data, algorithms, hardware and industry knowledge. ce
Stone Shi is executive editor-in-chief, Control Engineering China; https://www.cechina.cn Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
Stone Shi, Control Engineering China
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How industrial AI is impacting the plant floor
Control Engineering research: AI implementation is ramping up. Engineers are cautious and prioritize proven use cases like predictive maintenance over the AI hype cycle.
There is a tension at the center of industrial AI adoption: 65% of organizations are piloting or evaluating artificial intelligence (AI) and machine learning (ML) for automation and controls, yet 61% of professionals report low or no trust in AI-driven control systems.
That trust gap is shaping the next phase of adoption. AI can generate attention, but can it earn a place in environments where uptime, cybersecurity and safe, repeatable performance matter more than novelty?
Control Engineering's 2026 AI in Control Systems Report, based on responses from 107 professionals involved in specifying or purchasing control systems, shows an industry shifting from broad interest to targeted experimentation. Only 27% said AI or ML is in full production today, but pilots and proof-of-concept projects are widespread, showing manufacturers are looking for practical opportunities rather than waiting on the sidelines.
Select a problem worth solving
Predictive maintenance leads the list of AI/ML uses being deployed, tested or evaluated, followed by machine vision. Anomaly detection, robotics and quality-control applications are also attracting attention.
Industrial teams are applying AI/ML where it may help recognize developing equipment problems, identify production abnormalities or extract useful signals from growing volumes of plant data. Production optimization was the most frequently cited desired outcome, with predictive maintenance and faster troubleshooting following closely. These are familiar operational goals rooted in improving throughput, preventing unplanned downtime and finding the cause of a problem before it grows.
The case for AI augmentation
Despite the rhetoric surrounding AI, survey respondents do not expect it to displace the automation technologies running their operations. Four out of five said AI/ML will complement traditional approaches rather than replace them. The likely near-term model is an additional capability layer that supports engineers with earlier warnings, richer context and better-informed decisions. AI's value lies in extending traditional control logic and safety systems, particularly where patterns are too complex, data sets too large or troubleshooting takes too long.
AI faces a higher bar than many other digital tools. Cybersecurity emerged as the leading adoption concern, with data collection and use and return on investment following behind. Pilot projects are the leading strategy for building trust and quantifying results. A narrowly focused deployment can show whether an AI model improves maintenance planning or reduces investigation time without introducing unnecessary risk. Hybrid architectures combining
AI in control systems: By the numbers
27% Organizations using AI/ML in full production
65% Organizations piloting or evaluating AI/ML
42% Cite predictive maintenance as a leading application
52% Identify production optimization as a desired outcome
61% Report low or no trust in AI-driven controls
80% Say AI will complement, rather than replace, traditional automation
57% Cite cybersecurity as a top AI adoption concern
54% Use pilots to build trust and quantify ROI
on-premises and cloud-based models were the most popular deployment choice, while a substantial share of respondents favored on-premises-only systems. ce
Gary Cohen is senior editor, Control Engineering, gcohen@arrowfly.com. Amanda Pelliccione is marketing research manager; her report is at www.controleng.com/research.
INSIGHTS
How to justify automation, use AI to help robotics
Apply automation more effectively with tips on automation justification, collaborative robotics, industrial robots and AI.
Robotics, industrial artificial intelligence (AI) integration, and automation justification advice were among tips and trends from Automate 2026 from Association for Advancing Automation (A3). Think again about how to apply automation more effectively. See more advice at www.controleng.com/ how-to-justify-automation-use-ai-tohelp-robotics.
AI framework accelerates robotics
To help U.S. manufacturers better use AI for robotics, the ARM Institute is working on an ARM modular AI framework, said Matthew Powelson, senior robotics software engineer, ARM Institute. Powelson presented on “Accelerating AI-enabled robotics for manufacturing: Bridging R&D and commercialization.” The ARM Physical AI Framework is an agentready structure to provide a trusted, safe, structured way to build systems without unrestricted access to source code or machines, he said.
AI with trusted engineering tools
AI can accelerate use of industrial robotics, and integration with trusted tools can check AI validity to lower risk and increase safety, said YJ Lim, principal technical robotics product lead, MathWorks. AI can reduce what would have taken weeks to hours. In his session, “Accelerating Industrial Robotics with AI and Generative Models,” he said combining AI with trusted and validated tools can improve results with copilots, integrate with external generative AI agents, develop generative-AI-powered applications.
Automation justification
Automation needs more than a tradition-
al ROI calculation, and a member of the A3 Motion Control and Motors committee wants the group to develop a ROI+Plant Vitality Index to get more automation projects justified and approved. The suggestion was part of the session, “Making automation ROI practical and achievable,” from Christine Bush, Robotics Center of Excellence Leader, Schneider Electric. Considerations may include asset health modernization, automation levels, agility, digital integration and digital transformation, workforce readiness, sustainability performance and maintenance maturity.
Data for robot training
How much data do industrial robots need to learn to respond to variable challenges without specific, traditional programming for each? That’s among questions Erin McColl, director, Robotics Technology Adoption, Toyota Research Institute and her team are considering. The presentation discussed: “Bridging the gap: Toyota Research Institute’s approach to real-world robotics in manufacturing.”
Other sessions included...
Automating advice from a “State of the Automation Industry” keynote panel; Michael Mahfet, president, GCG Automation & Factory Solutions: “Why ROI is not one of the top reasons collaborative robots are justified by businesses.” In a keynote, Siemens Digital Industries, discussed digital twin advantages. A Standard Bots keynote cited more 150% more job growth among robot adopters. ce
Mark T. Hoske is editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
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Mark T. Hoske, editor-in-chief 847-830-3215, MHoske@Arrowfly.com
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Daniel E. Capano, senior project manager, Gannett Fleming Engineers and Architects, www.gannettfleming.com
Frank Lamb, founder and owner Automation Consulting LLC, www.automationllc.com
Joe Martin, president and founder Martin Control Systems, www.martincsi.com
Eric J. Silverman, PE, PMP, CDT, vice president, senior automation engineer, CDM Smith, www.cdmsmith.com
Mark Voigtmann, partner, automation practice lead Faegre Baker Daniels, www.FaegreBD.com
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Content For Engineers. WTWH Media focuses on engineers sharing with their peers. We welcome content submissions for all interested parties in engineering. We will use those materials online, on our Website, in print and in newsletters to keep engineers informed about the products, solutions and industry trends.
* Control Engineering Submissions instructions at https://www.controleng.com/connect/how-to-contribute gives an overview of how to submit press releases, products, images and graphics, bylined feature articles, case studies, white papers and other media.
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* If the content meets criteria noted in guidelines, expect to see it first on the website. Content for enewsletters comes from content already available on the website. All content for print also will be online. All content that appears in the print magazine will appear as space permits, and we will indicate in print if more content from that article is available online.
* Deadlines for feature articles vary based on where it appears. Print-related content is due at least three months in advance of the publication date. Again, it is best to discuss all feature articles with the content manager prior to submission. Learn more at: https://www.controleng.com/connect/how-to-contribute
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Latest automation mergers: May, June 2026
Automation mergers, acquisitions and investments include industrial automation, instrumentation and robotics. Bundy Group, an investment bank and advisory firm that specializes in the automation segment, provides an update on mergers and acquisitions and capital placement activity for this industry, in May and June, involving Honeywell Intelligrated, Ametek and GE Vernova, among other companies.
uAmerican Industrial Partners agrees to buy Honeywell Warehouse and Workflow Solutions, April 23 Honeywell agreed to sell its Warehouse and Workflow Solutions business to American Industrial Partners in an allcash transaction, a provider of material handling and warehouse automation solutions including automated sortation
systems, palletizers, conveyors, robotics, aftermarket services and software under Intelligrated and Transnorm brands with $935 million in 2025 revenue and 3,300 employees.
uAmetek acquired Indicor Instrumentation, May 6
Ametek acquired Indicor Instrumentation from Indicor LLC (backed by Clayton, Dubilier & Rice) for $5.0 billion in cash, a portfolio of mission-critical test-and-measurement businesses generating $1.1 billion in sales with strong recurring revenue from consumables and services.
uGE Vernova plans to acquire Robotech Automation, May 21
GE Vernova is acquiring Robotech Automation, a Quebec-based robotics and automation systems integrator with
Is AI fueling growth in chip equipment billings
SEMI, AN INDUSTRY ASSOCIATION for the semiconductor and electronics supply chain, reported in SEMI’s Worldwide Semiconductor Equipment Market Statistics (WWSEMS) report that global semiconductor equipment billings increased 14% year-over-year to $36.55 billion in first-quarter 2026. First quarter 2026 billings rose 1% from the prior quarter.
This is important for Control Engineering readers because motion controls and other advanced automation are used in semiconductor equipment, and the silicon that equipment produces is used in automation devices and systems.
Quarterly billings reached a record, driven by continued AI-related capacity expansion and technology upgrades for leading-edge logic, DRAM and advanced packaging.
“The strong start to 2026 reflects continued industry investment in the capacity and infrastructure needed to support AI-driven semiconductor growth,” said Ajit Manocha, SEMI president and CEO. “Record first-quarter billings highlight ongoing momentum in leading-edge manufacturing and advanced packaging.” Based on data submitted by members of SEMI and the Semiconductor Equipment Association of Japan (SEAJ), the WWSEMS report summarizes monthly billings for the global semiconductor equipment industry. ce
Edited by Puja Mitra, Arrowfly, for Control Engineering, from a
roughly 35 employees, to accelerate its robotics strategy and deploy automation across its supply chain and manufacturing operations. The company already works with GE Vernova on active projects, and the deal is expected to close in early Q3 2026. ce
Clint Bundy is managing director, Bundy Group, which helps with mergers, acquisitions and raising capital. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
Search on Bundy at www.controleng.com for more merger and acquisition news and trends.
Robot demand in the semiconductor industry is projected to outpace growth in other sectors. Read more at https://www.controleng.com/industrial-robots-and-ai-new-demand-and-capabilities Courtesy: Interact Analysis
Shri Vidya Selvan, Karthicraja Vellaichamy Munisamy and Vinoth Upendra Janardhanan, CDM Smith
How to rethink automation through design, practical engineering
A design mindset can complement traditional automation engineering to improve operator usability and alarm effectiveness without altering proven control strategies. An alarm management example demonstrates how small, human-centered improvements can deliver meaningful operational benefits during supervisory control and data acquisition upgrades.
Any automation project’s success is not arbitrary; it is built on strengthened engineering practices refined over many years and proven to deliver successful results. In the water industry, whether the project involves treatment plant expansion, a remote pumping station upgrade, or the modernization of an existing control system, the engineering process is usually structured, disciplined, and technically robust.
Most automation projects have a clear and logical workflow. A typical workflow starts with defining process requirements, followed by instrument selection based on operational needs, and developing framing control strategies based on client requirements and inputs from cross discipline coordination on how the process must be controlled. These strategies are then programmed into programmable logic controller (PLC) platforms and integrated with supervisory control and data acquisition (SCADA) systems that allow operators to monitor and control the process. In most cases, following these proven methods results in a system that performs exactly as expected.
When “working” leaves room to improve
However, even when an automation project is technically successful, there is often an opportu-
nity to improve how the system supports day-today plant operations. In many projects, the control system performs perfectly from a technical standpoint, yet operators may still find it tough to quickly understand the overall plant state—especially during abnormal conditions. This is not a downside; rather, it demonstrates the proven approach used in automation projects, where the priority is ensuring reliable system operation. This strong technical foundation sets the stage for further enhancements in how people interact with the system after commissioning.
A good example of this can be seen in a typical pumping station control system upgrade. Consider a station that has been operating reliably for many years using local control panels and basic level-based pump sequencing. As part of a modernization project, the station is upgraded with a new PLC system and integrated into a central SCADA platform. The project is executed using a well established engineering approach. Existing pump logic is recreated in the new controller, level transmitters are connected, alarms are configured, and the upgraded station is commissioned successfully.
But what if the same project had been approached slightly differently?
Instead of only recreating the existing control strategy, the engineering team could also have
controleng.com
KEYWORDS: Process control modernization, updating alarms, process optimization
CONSIDER THIS Don’t just modernize equipment; update the process, too.
ONLINE
Recent CDM Smith articles for Control Engineering include:
FIGURE 1: Designing for attention: Addressing alarm fatigue in supervisory control and data acquisition interfaces provides an example.
Graphics courtesy:
CDM Smith
evaluated how operators interact with the pumping station during real operating conditions. For example, how quickly can an operator identify why a pump is not starting? How easily can abnormal level trends be detected? Is the human-machine interface (HMI) display designed around the way operators think about the station, or is it simply a graphical representation of the equipment and instrumentation?
and how well the automation system can adapt to future operational needs.
If the same pumping station upgrade were approached using design thinking principles, the result would still be a reliable automation system. The difference is that the project would modernize the hardware, improve operator awareness, simplify system interaction, and create a more intuitive control environment.
‘Is the human-
machine interface (HMI) display display designed around the way operators think about the station?
’
Where design thinking fits in a process control upgrade
By asking questions like these during the design stage, the upgrade can deliver the same technical success while also improving operations and maintenance.
This is where the concept of design thinking becomes relevant in automation engineering.
Design thinking is an iterative, human-centered framework that integrates empathetic user understanding, creative brainstorming, and rapid prototyping that solves complex problems.
Because empathy is a key factor of design thinking, it guides solution providers to deeply understand the operators’ frustrations, needs, and behaviors rather than simply recreating what already exists.
Design thinking is not an alternative to traditional engineering practices. Instead, it strengthens them by encouraging engineers to design automation systems around how they are used in the field. It focuses on whether a system works and on how effectively operators can interact with it, how easily maintenance teams can support it,
In other words, traditional engineering methods already deliver strong technical results. Design thinking provides an opportunity to make those results even more effective. In this article, we explore how the concept of design thinking can be implemented in any automation project. The section that follows explores a step-by-step approach on how design thinking can be implemented and applied at each project phase, using alarm management as an example.
A practical example of design thinking in application engineering
Alarm management is one of the most common challenges engineers face during SCADA upgrades. In many utilities, the alarm system has evolved over years of operation through incremental changes. As a result, the existing system may contain:
• Duplicate alarms
• Irrelevant alarms
• Unclear alarm messages
• Inconsistent alarm priorities
• Frequently triggered alarms.
ANSWERS
This approach is technically correct and reliable. The plant continues to operate in the same way after the upgrade, and operators do not need to relearn the alarm management system. From a project delivery perspective, the migration is successful.
The engineering solution works, but it does not fully improve the operators’ experience. This situation allows engineers to apply design thinking to enhance an automation project.
‘Conduct operator interviews to understand which alarms they trust, which they routinely ignore, and why.’
How design thinking solves the same problem
Design thinking can be applied to any problem using several established frameworks; some emphasize discovery and validation, while others emphasize iterative prototyping. This article examines the “stop, think, observe, proceed” (STOP) framework because it provides an engineering-friendly problem-framing tool that emphasizes a “stop and think” moment, which encourages teams to intentionally pause and re-evaluate the problem definition before exploring solutions.
The STOP framework guides teams to pause, reflect on the problem, gather insights, and then move forward with solution development. This intentional pause helps teams validate the real operational need before implementing changes.
Applying the stop, think, observe, proceed (STOP) framework
In the sections that follow, we keep STOP in its original sense—stop, think, observe, proceed—and show how each step can be applied to alarm management during SCADA migration. The goal is to demonstrate a reusable approach: the same STOP steps can also be applied to many other automation design challenges. To illustrate how the STOP framework transforms alarm management, we walk through each step one at a time.
S – Stop (Stop unnecessary alarms)
The first step is identifying alarms that no longer provide value to operators. These may include:
• Alarms that are always active
• Duplicate alarms for the same condition
• Alarms that do not require operator action
Instead of migrating every alarm, the engineer evaluates whether each alarm helps operators make decisions. A practical approach includes:
• Facilitating an alignment workshop with operators and engineers to define what an “actionable alarm” means for this plant
• Defining guardrails to ensure a formal review is performed before eliminating/changing alarms used for protection or regulatory requirements
By only migrating the alarms that help operators make decisions, operations can avoid unnecessary alerts and improve focus. This sets the stage for the next step: fine tuning the remaining alarms to maximize effectiveness.
T – Think (Tune existing alarms)
In the “think” step, empathy enters the process. The team should think about how the current alarm system affects operators during real operating conditions, especially during abnormal events. Many alarms in older systems are technically correct but poorly configured. For example, high priority alarms may be assigned to minor events, or alarm limits may be set so sensitively that operators receive frequent false positives. Instead of jumping directly to solutions, start by understanding the operators’ experience by:
• Conducting operator interviews to understand which alarms they trust, which they routinely ignore, and why
• Developing a rationalization worksheet to map Alarm>Decision>Action.
These insights can help the team translate operator feedback into specific improvements, such as:
• Setting accurate priority levels
• Adjusting alarm limits to ensure sensitivity is balanced
• Clarifying alarm messages so they are easier to interpret.
These changes do not alter the control strategy, but they improve how alarms support operator decision making (set points, deadbands, delays, and priorities) and should be validated during testing. The next logical step is to optimize how alarms are structured within the system to make it easier for the operators to interpret plant conditions.
O – Observe
(Optimize the alarm structure)
The “observe” step focuses on improving how alarms are organized rather than changing their technical behaviors. The team can observe how the proposed changes perform in practice, both in system behavior and in the operators’ day-to-day experience. This may involve:
• Grouping alarms by process area
• Standardizing alarm messages
• Aligning alarm priorities with operational impact
• Simplifying alarm descriptions.
From an engineering perspective, this step is often configuration-focused, but it still benefits from structured review and operator feedback. By making the alarm structure more intuitive, operators reduce response times and minimize confusion during emergencies.
The outcome of this step should be treated as feedback. If data or operator feedback does not improve as expected, the team should loop back and re-think the assumptions (priorities, messages, grouping, and suppression rules). Practical observation measures should include changes in nuisance alarms, standing alarms and flood frequency, along with operator experience indicators such as time to acknowledge, time to diagnose, the frequency of ignored alarms, and reported levels of alarm fatigue.
Building on this foundation, the final step ensures these improvements remain sustainable.
FIGURE 3: Bridging the gap between traditional automation engineering and design thinking may work best.
ANSWERS
‘Apply configuration actions based on the rationalization decisions, such as adjusting deadbands, delays, set points, and priorities, and improving message clarity.’
P – Proceed
(Prepared for today and the future)
The final step, proceed, focuses on implementing the improvements identified in the Think and Observe steps, verifying that they work under real operating conditions, and putting practices in place to sustain them.
Practical methods include:
• Implementing changes incrementally (such as by process area or unit first), validating the results with operators, and proceeding to expand to other processes
Insightsu
Process control, instrumentation
design insights
uExplain how an automation project can be technically successful yet still benefit from improvements in operator usability, situational awareness, and day-to-day operability.
uDescribe how design thinking complements traditional automation engineering by using a human-centered, iterative approach to improve how operators and maintenance teams interact with programmable logic controller/supervisory control and data acquisition systems.
uApply the stop, think, observe, proceed framework to evaluate and improve alarm management during supervisory control and data acquisition migration without compromising reliability.
• Applying configuration actions based on the rationalization decisions, such as adjusting deadbands, delays, set points, and priorities, and improving message clarity
• Establishing governance to prevent backsliding by maintaining an alarm philosophy, using a simple management-of-change process for new/modified alarms, and conducting periodic reviews of nuisance alarms, standing alarms, and floods with operators.
Instead of treating alarm management as a onetime activity during migration, the engineer defines simple rules that prevent the same problems from appearing again. New alarms should be required to follow the same structure defined during this phase.
By embedding these standards, plants ensure their alarm systems remain manageable and effective for years to come, even as technology and processes evolve (Figure 2).
Why the combined approach works best
When traditional engineering practices are combined with design thinking, teams can
achieve measurable improvements in usability and operability, even when the underlying control strategy remains unchanged.
What changes is the decision-making approach. Instead of replicating the existing system exactly, the engineering team can use migration as an opportunity to make small, meaningful improvements that reduce operator burden and improve clarity during abnormal conditions (Figure 3). In the water and wastewater sector, where systems often remain in service for decades, these improvements can have a significant long-term impact. A clearer, more actionable alarm system reduces nuisance notifications that lead to alarm fatigue and operator stress, and it improves response time during abnormal conditions.
Alarm management is one clear example, but the same human-centered mindset can be applied to many parts of control system design, such as:
• Improving SCADA graphics and navigation
• Developing clear documentation and procedures
• Implementing consistent and reliable control strategies
• Performing operator training and plant maintenance.
Design thinking complements traditional automation engineering by providing control systems that are reliable, easier to maintain, and upgrade over a facility’s life cycle. ce
Shri Vidya Selvan, CDM Smith Inc., Chennai, India, is a mid-level automation engineer who designs and programs industrial control systems; Karthicraja Vellaichamy Munisamy, CDM Smith Inc., Chennai, India, is a mid-level automation engineer and an artificial intelligence proponent; Vinoth Upendra Janardhanan, CDM Smith Inc., Chennai, India, is a senior automation engineer and an artificial intelligence proponent who designs and programs industrial control systems. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
ANSWERS
Ed Bullerdiek, process control engineer, retired
PID spotlight, part 31: What is PID gap control?
How do you make a PID controller ignore small deviations while still responding to large deviations? What is a gap algorithm? Which algorithms work with which systems?
In the next three articles we are going to discuss how to use gap action to improve controller performance. This article will define four gap action algorithms and basic information you will need to know to implement these algorithms. The next two articles will cover:
• How to use gap action to get better disturbance rejection than you would get from fixed proportional-integral-derivative (PID) controller tuning.
• How to use gap action to ignore small deviations while still responding to normal process disturbances. This can help remove unnecessary noise from the process.
Understand PID controller gap action and know four gap algorithms.
Know how to identify the system’s PID algorithm and which gap action algorithm works best with each. Also Know the difference between the positional and velocity PID algorithm variants and which gap action algorithm can be used with each.
Know the implementation details for four gap action algorithms.
CONSIDER THIS
Do you understand why notch gap algorithm implementation details are important and how to test for faulty applications?
Gap action reduces controller response when the process variable (PV) is close to setpoint (SP), either by changing the controller gain or by modifying how the error is calculated. Which method you choose and how you choose to implement it will depend on the type of PID controller your system uses and whether that PID algorithm uses the “positional” or “velocity” form. Furthermore, the type of problem you are trying to solve will dictate which type of gap action algorithm you should use. Some systems have gap action built into their PID algorithm. Unfortunately, not all gap action implementations work well. Some have quirks that will perform poorly in certain situations, and some should not be used.
Therefore, your success will depend on understanding the details of your system’s PID algorithm and, if it has built in gap action, understanding the gap algorithm’s details also. Since system documentation very rarely gets deep into the details,
this article online walks through how to test your system. A few simple tests will tell you if your PID algorithm is of the “positional” or “velocity” type and whether a supplied gap algorithm will work well. (The oft repeated advice that you should test how your specific system works is especially important when it comes to advanced features. There are no standards and, based on my experience, sometimes insufficient regard for how these features will work in practice.)
Gap action using controller gain
If your control system uses the classical or series form of the PID algorithm gap action can be implemented by changing the controller gain. These algorithms multiply the integral and derivative action by the controller gain, thus maintaining the ratio between the three parts of the controller. The parallel algorithm requires changing gain, integral and derivative proportionally to provide gap action; it may be easier to modify the error calculation instead.
Two forms of controller gain gap action available on commercial systems include:
• Notch gain: Reduces controller gain when the PV is close to the SP. The controller gain inside the notch may go as low as zero. This should only be used with the velocity form of the PID controller.
• V-notch gain: The controller gain increases linearly with the distance of the PV from the SP (the error). The controller gain at SP may be zero. This will work with either PID form, but the behavior will be different.
Of course if your system allows external programming to change the controller gain you can perform any kind of gain manipulation you want.
‘Gap action reduces controller
response when the process variable (PV) is close to setpoint (SP), either by changing the
controller gain or by modifying how the error is calculated.
’
However, if your system uses the positional PID form the gain calculation must be continuous. Discontinuities (the gain makes sudden jumps) will cause the controller output to make sudden jumps. The velocity form tolerates discontinuities, but there are tradeoffs here also.
Gap action using controller error
If your system uses the parallel PID algorithm or does not permit a program to change the controller gain the other way to shape controller response is to change how the error is calculated. Here are two gap algorithms that have been used:
• Error squared: The error presented to the PID controller increases as the square of the distance of the PV from the SP.
• Floating SP gap: The SP is matched to the PV within the gap, resulting in an error of zero being presented to the PID controller. If necessary a rate limit may be applied to the SP within the gap to provide positive control if the PV is moving too quickly through the gap.
These two work with any form of the PID algorithm because the error is programmed outside the PID function. Some systems have a PID on error function that expects an error to be fed to the function (and no input for the SP). If your system does not offer this a regular PID controller can be used by connecting the error calculation to the PV input and locking the SP at zero. Depending on system design a custom operator interface may be required.
Notch gap gain
Figure 1 is a visual depiction of a notch gap gain application. There are three configuration parameters:
0.25 per 10% error.
• Positive gap; the gap above the setpoint
• Negative gap; the gap below the setpoint
• Gap gain multiplier (range 0-1); how much to reduce the controller gain inside the gap.
In Figure 1 the configuration is positive gap is 10%, negative gap is 20%, the controller gain is 2.0
FIGURE 1: Notch gap gain setup: Controller gain (K) = 2.0, negative gap = 20%, positive gap = 10%, gap gain multiplier = 0.2. All graphics courtesy: Ed Bullerdiek, retired
FIGURE 2: V-notch gap gain setup: Gain at zero error = 0, Gain slope =
ANSWERS
straint control, where being on the safe side of the constraint doesn’t require aggressive control. The controller output (OP) is included in Figure 1 to check whether the proposed scheme will fully open/close the output assuming the setpoint starts at 50%. This is calculated for a gain only controller. This is not necessary for a self-limiting process, but it is very useful when planning gap action for integrating processes. Figure 1 confirms that for this gap control setup the OP will fully close and open within 50% of setpoint, which makes me feel comfortable that this controller would keep a level from running dry or overfilling.
V-notch gap gain
A V-notch gap is shown in Figure 2 for a positional PID algorithm. The gap setup, however it is defined, requires a base gain when the error is zero and a slope for the gain on either side of zero. When used with the positional PID algorithm, this produces an S-shaped controller output. The controller will be less sensitive to small changes near the setpoint and will respond strongly to large errors. This does not provide the asymmetry needed for (for example) one-sided constraint control. However, you could write a custom version of the V-notch gap action with different slopes on either side of zero and/or a flat spot at the bottom. The key point here is for positional PID algorithms you cannot have a discontinuity in the controller gain.
Error squared gap
Figure 3 shows the effective controller gain profile for error squared gap and the controller output shape for gain only control. Error squared is functionally identical to V-notch gain but has the advantage that it will work with the parallel PID algorithm or any system that does not permit changing controller gain. The width of the notch is set by the scaling divisor; when the error exceeds the scaling divisor the PID controller is fed the error instead of the square of the error. This prevents excessive controller action on large errors.
and the gap gain multiplier is 0.2, which results in a controller gain inside the gap of 0.4 (2.0 * 0.2). Separate gaps allow (for example) a narrow gap, or no gap, on the side of the setpoint where a higher controller gain is required. This could be used for con-
The error squared algorithm does not permit a non-zero effective controller gain when the error is zero nor does it permit nonsymmetrical controller response to error. Therefore it will not be appropriate for constraint control.
FIGURE 3: Error-squared gap setup: Controller gain = 1.2, scaling divisor = 30%.
FIGURE 4: Floating setpoint gap: Controller gain (K) = 1.5, negative gap = 5%, positive gap = 10%.
Floating setpoint gap
The floating setpoint gap algorithm forces the SP to follow the PV inside the gap, thus calculating zero error. The controller output (OP) will remain constant as long as the PV is inside the gap. When the PV gets outside the gap the edge of the gap is used in the error calculation. This results in the OP profile seen in Figure 4, where the OP starts moving smoothly at both ends of the gap without objectionable jumps. The high and low gaps are configured separately, permitting an offset setup useful for constraint control. In Figure 4 the high gap is 10%, the low gap 5% and the controller gain is 1.5.
How fast the SP is allowed to follow the PV within the gap can be rate limited. This is not needed for self-limiting processes, however it can be used to prevent ping-ponging in integrating processes. (Integrating processes are notorious for bouncing between the high and low limits of a gap controller, confounding efforts at surge control. Providing a little restraint on the SP speed through the gap will provide some positive control of the
Onlineu
Five more pages of examples appear with this article online with 10 more graphics. System documentation very rarely gets deep into the details; this online version walks you through how to test your system. Sections cover:
• Identifying your PID algorithm
• Is my PID controller positional or velocity?
• Implementation details for notch gap gain
• Implementation details for V-notch gap gain
• Implementation details for error squared gap
• A final note about gap control.
Link to PID spotlights, parts 1-28 and with this article online, starting with “Three reasons to tune control loops: Safety, profit, energy efficiency.”
More on PID and advanced process control from Control Engineering https://www.controleng.com/control-systems/pid-apc/
PV, which will suppress if not outright stop the ping-pong effect.) ce
Ed Bullerdiek is a retired control engineer with 37 years of process control experience in petroleum refining and oil production. Send comments and questions to freerangecontrol@ameritech.net. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
Robust Ethernet Networks
• Unmanaged 10/100/1000 Mbps Ethernet switches
• Single mode and multimode ber optic switches and media converters
• Diagnostic switches for network troubleshooting
• PoE switches, mid-span splitters and injectors
• Wired and wireless IP routers for secure remote access
• Custom con gurations and outdoor-rated options available
Ryan Lindsey, Emerson
New sensor platform designed specifically for monitoring
A fit-for-purpose wireless sensing system provides remote visibility into asset condition and performance, minimizing the need for routine field inspections.
p erating a manufacturing facility, data or logistics center and many types of commercial buildings requires a wide variety of measurements. These are needed to provide operators and maintenance personnel with situational awareness, and wireless industrial sensors can help. There can be thousands of measurements required, each with an appropriate level of frequency and detail. Consider two hypothetical process manufacturing examples:
• A continuous exothermic reaction in a vessel that must operate at a critical temperature to stay between inefficiency and potential explosion.
• A 350 hp reciprocating compressor where an elevation in the surface temperature of a valve cover indicates a maintenance problem is developing, capable of causing a shutdown if not corrected.
Both call for a temperature reading, but with different requirements:
• The reactor reading is being used to control input of the reactants in conjunction with an automation host system, so it must be precise with frequent updates.
• The compressor reading is not subject to rapid change, nor is it necessary to have high precision. A value plus or minus a few degrees with updates every 15 minutes reported to the maintenance and reliability team is sufficient for trend monitoring and other purposes.
These call for different approaches. Countless process applications exist with many precision instruments and transmitters, critical for operations. Solutions for the second situation, and many other monitoring applications in industrial and commercial applications, require a different type of sensing system. Many industrial and commercial assets today are either not monitored or are checked only through periodic manual inspections, requiring personnel to be physically onsite, often at the point of measurement. This limited visibility often means issues go undetected until failure, leading to unplanned downtime, higher maintenance costs and unnecessary asset redundancy.
FIGURE 1: Emerson’s AMS Wireless Vibration Monitor uses WirelessHART communication to deliver vibration data without wiring or manual readings. Overall vibration readings can be easily integrated into any maintenance planning system and plant historian. All images courtesy: Emerson
The problems holding these solutions back are often cost and complexity. Returning to the initial example, the temperature transmitter required for the reactor is relatively expensive due to its criticality, calling for very high reliability and precision. The transmitter’s sensor is typically installed in a thermowell, which requires detailed design, along with a process penetration. Using the same approach for monitoring the compressor is overkill, hence the importance of designing more cost-effective sensors designed specifically for monitoring.
Why monitoring matters
Some industrial plants, and even some commercial facilities, perform carefully organized and scheduled manual rounds. During these rounds, technicians evaluate equipment condition at prescribed intervals with specialized equipment, such as vibration analyzers and infrared temperature readers, to look for developing problems. Even when these rounds are performed rigorously, high labor costs are incurred, along with exposure of personnel to hazards in some cases. Furthermore, measurements tend to be inconsistent, and issues can arise between rounds. In the worst case, rounds are performed with far less rigor, or maybe not at all. Fortunately, new sensing solutions provide answers to these and related problems.
Monitoring where it counts
Historically, where a plant or facility was heavily dependent on a few critical pieces of equipment, those likely had a range of key diagnostic devices permanently installed. All these monitoring components had to be installed and wired, often at considerable expense, which is why they were added only to the most critical pieces of equipment. There were many other installations that could have benefited from the same treatment, but the costs and complexity were simply too high.
More recently, the picture has changed. Process plants have adopted WirelessHART (FieldComm Group) widely for a variety of purposes, so networking infrastructure is often already in place. Consequently, adding diagnostic sensors using WirelessHART communication provides ways to monitor existing and new equipment at far lower cost since no wiring is necessary. Many such sensors (Figure 1) have been available now for more than 10 years, often using the same basic technology as more complex process transmitters. Compa-
‘Long update intervals allow for extended battery life, while reducing the amount of data to process and store.’
nies of all sorts have taken this as an opportunity to extend their monitoring deployments, but cost and complexity can remain limiting factors.
Fit-for-purpose sensing systems address issues
As mentioned, instruments and transmitters for process applications must provide high precision and reliability, with service life measured in decades. In most applications, such transmitters also send supplementary diagnostic data and secondary variables. Often, they must provide data updates every few seconds. This kind of capability is not required for most asset monitoring. Equipment condition is not likely to change suddenly outside of a catastrophic failure, nor is there much use for supplementary data, so little bandwidth is needed.
Returning to the opening example, if the reliability team receives an update on the compressor temperature every 15 or 30 minutes, this will likely be more than sufficient. In many cases, even once or twice per shift is still fully useful. When used with a WirelessHART sensor, long update intervals allow for extended battery life, while reducing the amount of data to process and store. These conditions eliminate the need for a full-featured transmitter, so sensors designed for monitoring can be more cost-effective. When more plant assets are equipped with automated monitoring, tedious and haphazard manual rounds can be eliminated, allowing technicians to concentrate on more useful tasks.
An industrial internet of things (IIoT) wireless monitoring platform can provide equipment monitoring sensors:
• Built around IIoT concepts using modular components.
• Designed for easy mounting, and moving to different locations.
Synchros Temperature Monitor is an easy-to-use wireless temperature monitoring device that reliably measures surface or ambient temperature. The initial Rosemount Synchros Temperature Monitor uses WirelessHART communication; other options are planned, such as cellular and LoRaWAN (LoRa Alliance), using the same housing.
COVER: Emerson’s Rosemount Synchros Temperature Monitors can provide sufficient temperature data taken from the pipe surface to calculate heat exchanger efficiency, determining when fouling or other issues are degrading operation.
FIGURE 2: Emerson’s Rosemount
ANSWERS
FIGURE 3: Emerson’s Plantweb Insight Applications provide pre-configured dashboards to collect and present diagnostic data. Data from a Rosemount Synchros Temperature Monitor can be integrated with maintenance and reliability platforms, such as AMS Device Manager and Plantweb Insight Applications.
FIGURE 4: Adding Rosemount Synchros Temperature Monitors to process equipment can verify that pumps and other critical equipment are functioning within normal parameters, such as this segment of a carbon dioxide capture unit. Other planned sensors include measurements for corrosion, pressure, vibration and others, all with the same look and feel, simplifying use.
• Suited specifically for asset monitoring measurements and data streams.
• With WirelessHART communications protocol.
• Using internal power so no wiring is required.
An IIoT temperature monitor (Figure 2) is designed for taking surface or ambient readings in a variety of applications, with simple mounting using a bracket or magnets for a flat surface, or hose clamps for pipe mounting. A WirelessHART repeater is also available, extending the range and reliability of the network where device counts are too low to provide sufficient wireless mesh network interaction.
This device can now be deployed in many applications where equipment temperature is an indicator of condition:
• Motor housings
• Compressor housings
• Bearings
• Gear boxes
• Heat exchangers
• Ambient temperature
• And others.
Wireless temperature monitor information can be integrated with maintenance and reliability platform applications (Figure 3).
Integrating wireless sensing with existing implementations
Wireless temperature monitors are designed to work in conjunction with process transmitters and other monitoring sensors. For example, this segment of a carbon dioxide capture unit (Figure 4) illustrates how they can be deployed alongside conventional temperature transmitters to deliver data for strategic equipment monitoring points. The monitors are not tied to the unit’s automation host
as they are not intended for process control, they are instead used to monitor the supporting equipment, such as pumps and heat exchanger exteriors.
Wireless monitoring evolution continues
Wireless temperature monitor can use WirelessHART communications. Other options are planned, such as cellular and LoRaWAN (LoRa Alliance). WirelessHART can integrate a large numbers of instruments in close proximity by exchanging large data packages at relatively fast update rates. This is often the case for monitoring sensors as well, however manufacturing assets requiring monitoring can frequently be more spread out geographically with fewer installations over a large space, such as in large logistics center.
In some environments, a different wireless protocol, such as LoRaWAN is better suited to networks where sensors are more widely dispersed , with greater distances between nodes. In very low density remote applications, cellular connections may even be necessary using the same housing, for deployment in these
environments. Sensor configurations using Bluetooth technology (Bluetooth Special Interest Group) is planned. In addition to temperature, other types of wireless devices are expected to measure parameters such as corrosion, pressure, vibration and others. These new sensor types will have the same look and feel, simplifying use.
Wireless monitoring sensors and supporting application platforms simplify the installation, configuration and maintenance of monitoring systems. These new sensors can be installed and brought online more quickly and cost-effectively than traditional wired transmitters. Given the gains of higher availability and the ability to assign scarce technicians to more valuable tasks, effective automated monitoring strategies can deliver payback in a matter of months. ce
Ryan Lindsey is a senior IIoT platform manager at Emerson. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
Insightsu
Wireless IIoT sensing and monitoring insights
uIndustrial monitoring matters for many critical applications; industrial internet of things (IIoT) monitoring can help.
uFit-for-purpose wireless sensing systems address challenges in many industrial applications.
uWireless sensors can integrate with existing implementations and will expand capabilities with other wireless platforms.
Geno Triana, CDM Smith
Seven practical tips to improve industrial cybersecurity
Operational technology environments require resilient standards, technologies, organizations, and practices.
Industrial cybersecurity is no longer a theoretical concern reserved for information technology (IT) departments. As industrial control systems (ICSs) become more connected—supporting remote access, enterprise visibility, and advanced analytics— the attack surface within operational technology (OT) environments continues to grow.
Effective industrial cybersecurity does not have to be overly complex or disruptive. A practical, risk-based approach, aligned with industry standards and operational realities, can significantly reduce exposure while maintaining system performance. The seven tips that follow reflect common lessons learned from assessing, designing, and supporting ICS networks across critical infrastructure and manufacturing environments.
1. A comprehensive assessment
Many organizations recognize the risk but struggle with implementation. Control systems are expected to run continuously, tolerate little latency, and support maintenance staff who are focused first on safety and uptime. Security measures that disrupt operations often face resistance, even when the risk is well understood.
Every cybersecurity program should begin with an honest assessment of the current environment. Without understanding how the system is built, operates, and is maintained, it is difficult to prioritize risk or justify investment. A comprehensive assessment is often conducted by a third-party familiar with industrial best practices, but organizations with mature internal capabilities may perform this work in-house. The assessment should define:
https://www.controleng. com/nist-releases-version-20-of-landmark-cybersecurityframework Online controleng.com u
FIGURE 1: Cybersecurity assessment flowchart shows steps involved in the process. Graphics courtesy: CDM Smith
• Organizational goals and operational constraints
• The current state of the control system and supporting network
• The desired future state based on risk tolerance
• A realistic capital improvement plan with scopes, schedules, and budgets for improvement projects.
In many cases, this effort can be combined with a formal cybersecurity risk assessment. A formal risk assessment allows your organization to quantify cyber threats, prioritize spending, and make highly targeted, actionable decisions. Successful assessments look beyond hardware and software. They evaluate the standards, technologies, organization and practices supporting the control system. A broader view often reveals that process gaps and unclear ownership pose risks equal any technical shortcomings (Figure 1).
2. Select an OT cybersecurity standard
Not all cybersecurity frameworks (CSFs) translate well into industrial environments. Selecting the right standard early helps align design decisions, policies, and long-term governance.
The ISA/IEC 62443 series is the most widely adopted ICS-specific standard. It provides a riskbased structure tailored for industrial automation and control systems, allowing organizations to apply security controls proportional to system criticality and risk. Many facilities also use the Purdue Enterprise Reference Model to structure network design. The Purdue Model separates the OT environment (Levels 0 through 3) from enterprise IT systems (Levels 4 through 5), typically using a demilitarized zone (Level 3.5). This separation remains a foundational best practice for reducing the spread of cyber incidents (Figure 2).
The National Institute of Standards and Technology (NIST) CSF is increasingly used to align IT and OT security efforts, especially when paired with ISA/IEC 62443. Its six core functions—Govern, Identify, Protect, Detect, Respond, and Recover— map well to industrial environments when adapted to OT constraints. Before adopting any standard, organizations should understand the operational impact. For example, physical separation of IT and OT networks may be cost-prohibitive compared to virtual segmentation for geographically dispersed IT/OT systems that share WAN infrastructure. Controls that work well in an IT environment may
introduce unacceptable latency or complexity in control systems if applied without modification.
3. Segment OT networks
Network segmentation is one of the most effective tools for limiting cyber risk in industrial environments, but it must be implemented carefully. Firewalls or data diodes should be considered to isolate the control system network from enterprise or other external networks. In systems requiring high availability, firewall redundancy should be evaluated to avoid introducing single points of failure. Physical or virtual segmentation can significantly reduce risk by limiting lateral movement and the spread of incidents. Segmenting networks by process area or criticality ensures that a problem in one segment does not cascade across the facility. Excessive routing between networks can introduce latency and jitter that affect deterministic protocols. The goal is intentional segmentation—not maximum segmentation—that aligns with system architecture and operational requirements.
Purdue
useful to understand how cybersecurity segmentation might be applied.
Designing a network with intentional segmentation requires expertise in network design and OT device application programming. OT networks designed exclusively by personnel with IT networking experience often fail to account for traffic patterns. If the person assigning your IP addresses does not understand that your equipment drives do not frequently communicate with each other, placing these drives on a separate network from the programmable logic controllers that monitor and control them can lead to excessive routing that degrades network performance (Figure 3).
4. Role-based user authentication
User access remains a frequent entry point for cybersecurity incidents. Weak credentials, shared accounts, and insufficient access controls are still common in control system environments.
User authentication requirements should complement physical access control measures, such as video surveillance, door locks with badge readers, and network switch port security. Authentication requirements for users accessing the system from a secured control room should be different from users accessing the system remotely.
Insightsu
INDUSTRIAL CYBERSECURITY INSIGHTS
uExamine best practices for safeguarding a control system from modern cybersecurity threats.
uUnderstand key considerations for planning a network segmentation approach and choosing an industry standard.
uReview the importance of maintaining cybersecurity throughout the life cycle of the control system.
FIGURE 2:
Reference Model is
ANSWERS
how information technology applies
Remote access should require multi-factor authentication and encrypted communications. Remote access solutions should be tightly controlled, monitored, and disabled when not actively needed. Each control system user should have unique credentials with access limited to job functions. Privileged access should be granted sparingly, reviewed regularly and logged. In most control system environments, authentication should be designed to work locally to ensure system availability, instead of relying on external services. Many facilities implement local authentication services so users can access systems when external connections are unavailable. Authentication controls must support reliability, not undermine it.
5. Monitor OT networks continuously
Preventive controls alone are not sufficient. Industrial cybersecurity requires visibility into what is happening on the network in real time. Passive network monitoring sensors can establish baselines of normal traffic and detect anomalies without disrupting operations. Because industrial communication patterns are often predictable, deviations can be strong indicators of equipment failure or malicious activity. When combined with log aggregation and security information and event management platforms, monitoring tools enable faster detection and response. Even basic alerting can help reduce the time to detect issues and may limit incident impact. Monitoring should be thoughtfully designed to support operations; avoid unnecessary alerting that can overwhelm staff (Figure 4).
6. Address staffing, training gaps
People are one of the most overlooked and exploited elements of industrial cybersecurity. Organizations should evaluate if they have sufficient OT cybersecurity expertise and consider dedicated cybersecurity roles, cross-training existing staff or leveraging trusted third-party help. Awareness training is needed for control system users. Engineers, operators and technicians are frequent targets of phishing and social engineering because they often have elevated access. A formal cybersecurity awareness program helps staff recognize threats and respond. Training materials from government agencies and reputable third-party providers can accelerate development and improve consistency. Operators should recognize and deal with cyber risks as part of day-to-day responsibilities.
7. Cross-functional governance
Cybersecurity is not a one-time project. A formal governance committee provides structure and accountability using representatives from IT, network security, operations, engineering, and maintenance. Typical responsibilities include written policies and procedures, risk and cybersecurity assessments and reviewing new technologies to reduce risk and intelligence on new cyber threats. ce
Geno Triana, CDM Smith, Dallas, Texas, is a senior automation engineer at CDM Smith. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, Arrowfly, mhoske@arrowfly.com.
FIGURE 4: An example architecture for operational technology network monitoring may differ from
cybersecurity.
FIGURE 3: Process-based network segmentation structure is shown.
Strategic data management and infrastructure: Building the physical foundation for the factory of the future
From the physical cabling "nervous system" to off-site data centers, here are the key pillars for managing the influx of real-time industrial data.
The signature of successful “factories of the future” is increasingly found in strategic data management. While physical throughput metrics ultimately determine business objectives, it’s how manufacturers are monitoring and managing an influx of data that’s guiding efficient operations. Because technology is becoming increasingly smart and digital, operators need to have the critical infrastructure in place, on and offsite, to digest data and translate it into actionable insights. Several key pillars that should guide data management regardless of industry or application.
Cable assembly selection, future
Data transmission should be seen through a physical lens. Companies must consider the proper cabling requirements that ensure smart devices successfully send real-time data to a controller or central platform. Factory layout, in brown and greenfield settings, must be considered when routing cabling to ensure an efficient data transmission and as safe work environment to hide cables from human workers and machinery. Cabling needs to be robust enough to handle the massive increase in data. Copper cabling and Ethernet offer low-latency benefits that are vital to successful processes. Any lag or disruption in data transmission can be costly for operators, especially when it impacts how decisions are made and how technology is operating. Cabling with sturdy, metal-plated connectors and resistance to electromagnetic or radio frequency interference is important to maintain transmission integrity. Watch for interference as smart solutions are added.
Cabling serves current data demands and as a
baseline for future operations. Companies need to proactively determine how much on-site data (and cabling) they’re able to manage, including through edge devices, and how they will approach potential future data center integration. Enterprise data centers are growing in adoption, especially with operators that leverage a considerable amount of artificial intelligence (AI) or smart technology. A data center can process large quantities of data gives operators more breathing room to expand and monitor their data-driven processes.
Integration can be subjective. Operators must understand what data needs to be transmitted and available on-site and what can be processed at an offsite. Often operators need to have the most relevant, immediate data to adjust their processes and respond to needs quickly. In-depth data management, analytics, diagnostics and predictive/preventive maintenance are usually best conducted with increased processing available at a data center.
Data centers offer increased security measures and redundancy in case data processing on-site is compromised or offline. Companies may benefit from a roadmap that considers a hybrid on-site/offsite setup to best manage data, including through an enterprise data center. ce
Dustin Guttadauro is product line manager for L-Com, an Infinite Electronics brand. Edited by Gary Cohen, senior editor, Control Engineering, Arrowfly, gcohen@arrowfly.com
2026 Control Engineering Product of the Year winners announced
®
A defining strength of the Product of the Year program is the direct involvement of Control Engineering’s subscriber audience. Readers reviewed nominated products and evaluated them based on technological advancement, service to the industry and overall market impact. Their votes determined this year’s 27 winners across nine categories, including one product earning the Most Valuable Product honor — the program’s highest distinction.
https://www.controleng.com/2026-ce-product-of-the-year-winners-announced/ 2026 Control Engineering Product of the Year
New engineering tool adds AI support for automation tasks
Beckhoff TwinCAT 3 CoAgent for Engineering (TE1700) is an open-architecture engineering assistant for automation development that supports multiple large language models and AI platforms. Integrated into the TwinCAT environment, the tool is intended to assist with programming tasks and help engineering teams reduce repetitive work during project development. The software is part of ML and AI tools. The assistant operates within the TwinCAT XAE engineering environment and provides code suggestions, optimization support, AI-assisted I/O configuration, HMI support and documentation updates.
Beckhoff, www.beckhoff.com
Innovative helical racks, less lead time
KHK USA Inc., a factory-owned distributor of KHK metric gears, announced KHK Ground Helical Rack Series, including KRHG, KRHGF and KRHGFD models. The racks are designed for machine tools, high-speed automation equipment and positioning systems that require accurate motion control and reduced operating noise. They are made from SCM440 alloy steel and undergo thermal refining and induction hardening.
KHK USA Inc., khkgears.us
New HMI update combines faster screens with richer tools
AutomationDirect has released version 9.00 of AutomationDirect C more CM5 series programming software for HMI development. The release has up to 250% improved screen performance over previous versions, added line and step charts with dual Y axes and dynamic scaling, an SVG-based graphics library, global find/replace for tags and text, object search, a consolidated object library, dynamic meters and alarms, a new log manager, flexible color selection and broader PLC driver support.
AutomationDirect www.automationdirect.com
CNC platform: faster machining, reduced scrap
Mitsubishi Electric Automation Inc. introduced the Mitsubishi M8V CNC Series for machine tool applications in North America. Based on the M80 Series platform, the series adds control functions intended to improve cycle times, support precision machining, extend tool life and reduce scrap in customized machining environments. Built-in wireless LAN allows operators to connect machines to software tools on a remote PC for wireless data exchange. It enables approximately 11% faster machining than the previous series through machine response-contour control. It has cutting load control to reduce cycle times and support tool life. Cutting point control compensatesfor changes in tool geometry.
Mitsubishi Electric Automation Inc., https://us.mitsubishielectric.com/fa/en
Power platform eases automation enclosure design
The Rittal RiLineX power platform is designed to speed design and assembly of automation inside industrial enclosures. The base systems with pre-assembled complete board contains standardized copper bars providing contact hazard protection up to IP2XB, which may be extended to IP 3X with accessories. Rittal, www.rittal.com
Edge execution reduces integration complexity
Rockwell Automation Inc. announced Rockwell FactoryTalk ResilientEdge, an execution architecture for automated manufacturing operations across highly-automated environments. Built on Rockwell FactoryTalk Optix and integrated with products including Rockwell Plex Manufacturing Execution System (MES), FactoryTalk ResilientEdge provides a common execution layer across machines, people and production systems.
Elmo's new motion controller and new servo drives for industrial applications, can be used in harsh operating conditions. The products were presented at Automate 2026 in Chicago. The new releases expand Elmo’s Platinum line and introduce the Titanium line. Designed for compact multi-axis systems, they include functional safety features integrated at the drive level, which can reduce the amount of external safety hardware and cabling required. The Titanium line supports multi-axis system design. Elmo Motion Control, www.elmomc.com
Speed design with latest geometric deep learning
Siemens Simcenter advanced engineering simulation and test portfolio expanded with Siemens Simcenter PhysicsAI software, which allows engineers to evaluate design concepts and variations more quickly. Simcenter PhysicsAI uses geometric deep learning technology from Siemens’ Simcenter STAR-CCM+ software to support faster AI-based design exploration. The software enables engineers to create AI reduced-order models faster from computational fluid dynamics simulation data and conduct design exploration; up to 1,000 times faster, using fewer computational resources than traditional methods. Siemens, www.siemens.com
CNC control for router applications
The Emerson Synchros Industrial Internet of Things (IIoT) platform is a suite of technologies for monitoring asset condition and supporting maintenance planning through a wireless architecture built for deployment in existing facilities. In many operations, asset monitoring still relies on periodic manual inspections due to cost and logistical constraints, which can leave emerging issues undetected between rounds. The platform lets users scale coverage over time on a shared platform designed for existing infrastructure. Emerson, www.emerson.com
New Ethernet switch adds timing and redundancy features
HMS Networks introduced HMS N-Tron NT7000 Series, a managed industrial Ethernet switch platform for fast recovery, time synchronization and rugged operating conditions. They are designed for applications with increasing network speed and complexity, where uptime is important. The NT7000 Series supports traffic pass-through in less than 7 seconds at startup and N-Ring redundancy with about 20 ms healing to reduce downtime after network faults. It supports hardware-based IEEE 1588 PTP for sub-microsecond time synchronization. HMS Networks AB, www.hms-networks.com
The Drives & Motion division of Yaskawa America Inc. released Yaskawa Compass 2, a CNC software solution for Yaskawa iCube Control. It combines the scalability of an industrial PC for user interface and path planning with the real-time motion and logic control of Yaskawa’s iC9200 machine controller, providing machine tool builders with a CNC control platform. Yaskawa America Inc., www.yaskawa.com
Entries are due Friday, August 28, 2026
Who should enter?
If you’re a system integrator with demonstrable industry success, Control Engineering and Plant Engineering urge you to enter the 2027 System Integrator of the Year competition. Past System Integrator of the Year winners—Class of 2026, Class of 2025, and Class of 2024—are not eligible to enter the 2027 System Integrator of the Year program.
What’s in it for the winners?
The chosen System Integrator of the Year winners will receive worldwide recognition from Control Engineering and Plant Engineering . The winners also will be featured as the cover story of the Global System Integrator Report, distributed in December 2026.
How will the competition be judged?
Control Engineering and Plant Engineering ’s panel of judges will conscientiously evaluate all entries. Three general criteria will be considered for the selection of the System Integrator of the Year:
• Business skills
• Technical competence
• Customer satisfaction
For more information on how to enter and proper criteria, visit: www.controleng.com/system-integrator-of-the-year
Submit today!
Entries due August 28, 2026
In order to be considered for the SI Giants program, your company must have a complete, valid listing within the Global System Integrator Database, and the entry form must be completed truthfully and accurately.
more information on how to enter and proper criteria, visit:
Rick Ellis, Director, Audience Growth 303-246-1250, REllis@Arrowfly.com
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Art director Mike Smith, MSmith@Arrowfly.com
Information: For a Media Kit or Editorial Calendar, go to https://www.controleng.com/advertise-with-us.
Letters to the editor: Please e-mail us your opinions to MHoske@Arrowfly.com. Letters should include name, company, and address, and may be edited.
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Less energy costs. More performance.
Driving Efficiency: DR2C Permanent Magnet Motor
Engineered for ultra-premium efficiency, the DR2C Permanent Magnet Motor from SEW-EURODRIVE delivers the performance today’s operations require while reducing long-term energy costs. With up to 50% lower energy losses than standard IE3 motors, the DR2C reduces total cost of ownership (TCO) and enhances reliability. Built with Interior Permanent Magnet (IPM) technology, offering high torque density in a compact, space-saving design, enabling smaller motor sizes without sacrificing power. Optimized for continuous duty and high-cycle start/stop operation, the DR2C performs efficiently across a wide speed range. Ideal for conveyors, automated logistics, packaging lines, and manufacturing systems.
