How Demon AI boosts manufacturing efficiency

🚀 IO-Link vs AI in Predictive Maintenance – What’s the Difference? In automation, IO-Link masters and hubs are already powerful. They provide device-level diagnostics like: ✅ Cable break detection ✅ Short circuit alarms ✅ Cycle counters & overload warnings This is what we call rule-based predictive maintenance – the device itself knows when it’s about to fail, based on pre-set thresholds. But here’s where AI takes it further: 🔹 Learns patterns from historical + real-time sensor data 🔹 Reduces false alarms by distinguishing process variation from actual faults 🔹 Predicts system-level failures (e.g., gearbox wear from combining vibration + current + temperature data) 🔹 Continuously adapts to changing conditions 👉 In short: • IO-Link = Local diagnostics (reactive + basic predictive) • AI Predictive Maintenance = System intelligence (adaptive + forward-looking) Both are essential. IO-Link gives visibility at the device level, while AI gives foresight at the system level. Together, they make factories truly Industry 4.0 ready. #PLC #RaspberryPi #Arduino #AIIntegration #Industry40 #SmartFactory #AutomateYourDreams

🚀 AI Meets Industrial Automation – A New Milestone at System 42 🔧🤖 We’re excited to announce a breakthrough from our R&D team at System 42. Our engineers have successfully integrated a custom edge AI system, the SynbAIoT, with an industrial Programmable Logic Controller (PLC) — enabling real-time observation and intelligent interpretation of digital input signals on the factory floor. The SynbAIoT is an AI-powered edge gateway that includes a hybrid data acquisition and inference unit with supervisory SCADA interface. It is din-rail mountable and plug-and-play. This achievement proves the power of combining AI with industrial automation in a non-intrusive, passive way: ✅ Continuous monitoring of PLC signals ✅ Real-time anomaly and pattern detection at the edge ✅ Actionable insights to inform smarter decision-making Why does this matter? Traditional PLC systems execute fixed logic. By augmenting them with AI-driven observation, we unlock new layers of visibility into machine behavior — helping detect irregularities, anticipate issues, and optimize processes without altering the existing control logic. This milestone brings us closer to our vision of the intelligent, autonomous factory — where AI enhances automation with insight, adaptability, and foresight. A huge thank you to our talented engineers, control specialists, and AI researchers for making this possible. 🙌 At System 42, we’re laying the foundation for a global platform for PLC AI — powering the next era of smart, connected industry. 🌍⚡ #System42 #IndustrialAI #SmartAutomation #EdgeIntelligence #ControlSystems #DigitalTransformation #Industry40 #ManufacturingInnovation

When Field Communication Meets Intelligence: The Next Step for Smart Plants In my last post, I talked about how Foundation Fieldbus forms the communication backbone of a process plant — that silent, reliable link between sensors, valves, and control systems. But here’s the exciting part: What happens when you connect that communication backbone to Artificial Intelligence (AI) and predictive diagnostics? We move from knowing what happened to understanding what’s about to happen. Each device on a Fieldbus network — whether it’s a pressure transmitter or a control valve — constantly shares detailed status data: temperature, signal strength, calibration health, valve position feedback, and even internal diagnostics. Traditionally, most of this data was ignored or underused. Now, AI can analyze it in real time and learn patterns that even experienced engineers might miss. 🔍 It can detect a valve actuator that’s taking slightly longer to respond. ⚙️ It can spot a transmitter that’s drifting slowly out of calibration. 📊 It can alert maintenance teams days before an alarm even appears. This is where smart communication meets smart analytics — and it’s changing how plants operate. When your field devices can talk, and your system can listen intelligently, you get: ✅ Fewer shutdowns ✅ Faster root-cause analysis ✅ Longer equipment lifespan That’s the future of ICSS and process automation — one where communication, diagnostics, and intelligence all work hand in hand. We’re not just automating anymore — we’re teaching our systems to understand themselves. #Automation #AI #FoundationFieldbus #ICSS #PredictiveMaintenance #ProcessControl #SmartIndustry

AI and PLCs — From Fragmentation to Standardization When PLCs first arrived on the factory floor, they weren’t the universal controllers we know today. Early on: Hardware was fragmented.  Logic was basic — engineers had to build custom functions from scratch. 💡 Over time, PLCs matured.  Standard modules, reusable function blocks, and libraries made them plug-and-play.  Even when customization was needed, it became faster and easier. That’s when scale and adoption truly accelerated.  ⚡ In my view, AI/GenAI today feels a lot like those early PLCs.  The power is there — but models aren’t yet plug-and-play.  A predictive maintenance algorithm that works in one plant doesn’t seamlessly transfer to another… even for the same equipment.  It may never be 100% plug-and-play — every asset, operation, and application is unique.  But can we accelerate AI’s scalability and adoptability the way PLCs did over time?  👉 Will AI evolve into reusable, industry-ready modules?  👉 Or will it always remain contextual, needing domain-specific engineering every time?  That’s how I see it — curious to hear your perspective.  #Industry40 #ArtificialIntelligence #SmartFactory #EAM #PredictiveMaintenance 

Thrilled to share news from Emerson: the Next-Generation PACSystems™ IPC line is now available — designed to bring AI-driven intelligence directly to the factory floor. What's really exciting is how these rugged IPCs are built for real industrial environments while enabling smarter operations. They offer: • #AI capabilities to support predictive maintenance, visual analytics, and process optimization • Real-time insights that help reduce downtime and boost throughput • Scalable, flexible edge solutions that bridge plant floors and enterprise systems This launch is a powerful step toward making advanced automation accessible and reliable in the toughest settings. Read the full news here: https://lnkd.in/dKtHPuda

The hardest industrial problems aren’t solved with one algorithm. They’re solved with a playbook of strategies. Experts who run complex processes all say the same thing: use a set of approaches and strategies and switch between them depending on conditions. After years of experience, that switching between strategies becomes internalized and instinctual. Intelligent autonomy can be done the same way. One glass manufacturer broke down their expert operator's approach into seven strategies. Each one became a "skill agent" - a specialist for one part of the process. In simulation, those agents trained through millions of scenarios. Stable runs. Disruptions. Edge cases. Learning to coordinate those seven strategies together like an expert does in real time. But while that human expertise takes years (or decades) to develop, the agent took weeks. In deployment, the fully trained agent system managed 90 sensors and 60 control actions in real time. It mirrored the operator’s decision-making and even surfacing new strategies he hadn’t considered. That’s exactly what intelligent autonomy can do: capture those strategies, coordinate them, and turn instinct into a system that scales. #industrialAI #intelligentautonomy #industrialautomation #industry40

🔐 From Purdue to AI-Aware Industrial Systems: A New Chapter for Critical Infrastructure For decades, the Purdue Model has been the backbone of industrial control systems, from sensors and PLCs 🧩 at the field level (0-1), to control and supervision ⚙️ (2-3), and up to enterprise IT systems 💻 (4-5). Together with IEC 62443, it helped structure how we protect operations, define security zones and conduits, and manage risks across sectors like energy ⚡, transport 🚆, water 💧, manufacturing 🏭, and healthcare 🏥. In parallel, IEC 61508 has guided how we keep those systems safe, ensuring that even when failures happen, people and processes stay protected. But today, something new is happening. Artificial Intelligence 🤖 is finding its way into operational environments, powering predictive maintenance, optimizing production, improving quality, and sometimes even making decisions in real time. This brings new assets (AI models, training data, inference engines, data pipelines) and new risks (model poisoning, data drift, adversarial manipulation). The truth is, our current frameworks like Purdue and IEC 62443 weren’t built with AI in mind. They don’t yet describe how to secure AI pipelines, govern model updates, or define trust boundaries when AI systems interact directly with control logic. And in safety-critical operations, a wrong AI prediction could directly impact physical safety, an aspect that IEC 61508 will soon need to address more deeply. As our industries evolve, new ideas will need to emerge, adding AI-specific layers between IT and OT 🧠. This means new zones, conduits, governance models, and monitoring systems that treat AI like a first-class citizen in industrial design. In the near future, resilience in critical infrastructure won’t just depend on IT and OT convergence, it will depend on the convergence of IT, OT, and AI, forming the next generation of secure, intelligent, and safe industrial ecosystems 🌍. #CriticalInfrastructure #IndustrialCybersecurity #AIinOT #IEC62443 #FunctionalSafety #IEC61508 #PurdueModel #CyberPhysicalSystems #SmartIndustry #OTSecurity #AIConvergence #Resilience

With the advancement of Industry 4.0 and manufacturing automation, industrial laser distance measurement has quietly become the “key sensory organ” of modern production systems. From a market perspective, the scale of laser sensors continues to grow — driven not only by the demand for high-precision measurement, but also by the rapid rise of non-contact distance sensing across various industries. Leveraging its dual advantages of high accuracy and real-time responsiveness, industrial laser distance measurement plays an irreplaceable role in automated production: ✅ Real-time Position Feedback: High-frequency ranging modules accurately capture motion data of moving targets, ensuring robots and automated equipment follow preset trajectories — reducing production errors and improving efficiency. ✅ Dynamic Quality Inspection: On high-speed production lines, laser sensors quickly detect product size or positional deviations, enabling “continuous precision inspection” and strengthening quality assurance. ✅ Condition Monitoring & Early Warning: Continuous monitoring of key component displacement allows for instant alerts when abnormalities occur, preventing overload or damage and minimizing production losses. More importantly, laser sensing technology is rapidly evolving toward integration, intelligence, and multifunctionality — embedding AI and machine learning into sensing systems to propel industrial automation from “precision perception” to “intelligent perception.” As market demand continues to expand, industrial laser distance measurement will keep driving innovation and injecting new “precision momentum” into automated manufacturing. 💡 What innovative applications of laser ranging technology have you seen in your industry? #Industry4.0 #Automation #LaserSensors #IndustrialLaserDistanceMeasurement #SmartManufacturing

💻⚡️📷 From PLCs to AI: Real-time Bottle Detection with a Custom YOLO Model In industrial automation, cameras are becoming just as important as sensors. In this project, I trained and implemented a custom object detection model to recognize and analyze bottles moving on a conveyor line. The model detects three elements in real time: the bottle, the label, and the cap. The goal was to simulate a vision-based inspection system that could be used for: detecting missing labels or caps, verifying product integrity, enabling smarter quality control directly on the line. What’s special here is that the model wasn’t downloaded or pre-trained for this task - it was custom-built and trained from scratch for this exact application. That means it understands the specific environment, lighting conditions, and product variations of this production setup. AI and computer vision are not replacing traditional automation - they’re becoming its new layer of intelligence. Combining classic PLC logic with real-time vision data opens up new possibilities for autonomous decision-making, predictive quality, and continuous process improvement. 💡 Question: Have you ever tried integrating vision systems or AI models into your automation projects? Where do you see the biggest potential for this kind of technology on the shop floor? #ai #computervision #automation #industrialautomation #machinelearning #yolo #deeplearning #qualitycontrol #smartfactory #plc #visioninspection #mechatronics

💯 Awesome article talking about the future of the packaging industry and developments being made with AI, motion control, digital twins, and simplified connectivity. ⚡ Siemens is helping OEMs navigate cost challenges, the demand for smarter and more efficient operations all while driving a significant shift towards advanced technologies. https://sie.ag/44PK2E