OTee’s Post

Software calibration is something that has been thrown around quite a bit of late. Here's a video explaining the fluff from the fact! Do watch this video from Liam Bates to understand calibration of IAQ monitors. #IAQ #IAQmonitors #workplacewellness #IWBI #Kaiterra #PM2.5

There's a wealth of OT data just waiting to be tapped into for valuable insights. While there are common excuses for not leveraging this data, compatibility and configuration challenges shouldn't be among them. Let's dive into how we can overcome these hurdles. 1️⃣ Compatibility Issues: Problem: Older PLC models not supporting modern communication protocols and incompatibility between different PLC brands and systems. Solution: - Use communication gateways and protocol converters: These tools can bridge compatibility gaps and ensure smooth data flow. - Invest in multi-protocol devices: Devices that handle various communication standards can simplify integration. Lots of companies sell these devices (we've used Control Solutions out of Minnesota in the past to convert Modbus to Ethernet). 2️⃣ Configuration Challenges: Problem: Misconfiguration of communication protocols and incorrect network settings can lead to communication failures. This is often a tricky issue that requires detailed debugging. Solution: Power through it ( 💪 ): Either bring in an expert to tweak the settings, or set aside a few days to figure things out yourself. You wouldn’t walk away from the challenge if a belt conveyor stopped working, so approach data configuration with the same determination. The Internet is filled with tutorials on how to debug every model of equipment (just google “PLC debugging tools”). Heck, you can even use ChatGPT to assist in debugging. Overcoming compatibility and configuration challenges is crucial for unlocking the full potential of OT data. They might seem scary, but many people have already solved these issues! How about your experience? Have you ever faced a PLC that you couldn’t connect with? And if so, what tools do you use to try to solve this issue? #plc #manufacturing #industry40

Are your (#PLC) logs with valuable data scattered around the factory? Read this ‘top secret’ blog to find inspiration on how to solve this problem with the Elastic #ELK stack!

Why PID Controllers Remain the Gold Standard in Control Systems Despite the hype around more complex control methods, the humble PID (Proportional-Integral-Derivative) controller continues to outperform in many real-world applications. Here's why: 1. Simplicity: PID's straightforward structure makes it easy to implement and tune. 2. Reliability: Decades of use across industries have proven PID's robustness. 3. Computational Efficiency: PID requires minimal processing power compared to MPC or neural networks. 4. Model-Free: Unlike MPC or LQR, PID doesn't need an accurate system model. 5. Interpretability: Engineers can easily understand and explain PID behavior, unlike "black box" RL or NN approaches. 6. Real-Time Performance: PID's quick response is crucial for many applications. 7. Cost-Effective: Lower implementation and maintenance costs than complex alternatives. While MPC, LQR, RL, and NN controllers have their place, PID remains the go-to solution for a wide range of control problems. Its balance of simplicity and effectiveness is hard to beat. What's your experience with PID vs. other control methods? Share your thoughts below! #ControlSystems #PID #Engineering #Automation

Ever wondered how leading manufacturers are staying ahead? Our latest article explains transformative power of Elastic Observability in the manufacturing sector. Discover how Elastic is optimizing operations, enhancing product quality, and driving innovation. 👉 Read the article here: https://lnkd.in/eGrhniXF #Manufacturing #Innovation #Elastic #PredictiveMaintenance #PLC #Logs #Industry40 #Observability OECO Groep Cronos.AI | Enterprise AI

I spoke on “Technologies and innovation accelerate digital transformation for operational excellence” at #FEA2024. The approach to industrial transformation has matured. It is becoming clear to all that plants don’t have all the data they need, and that data science is not the answer to all use cases. The emphasis was on #innovation automating manual data collection with #WirelessSensors and mechanistic #AI for threat monitoring to become more situationally aware for greater #OccupationalSafety, for performance monitoring to become more responsive for greater #sustainability and lower energy cost, for #ConditionMonitoring to become more predictive for greater #reliability and reduced #maintenance cost, and for balance process monitoring to become more productive for greater production throughput and reduced off-spec product. I covered the 30+ most effective use-cases across these domains. #Technology-wise I covered the #NAMUR open architecture #NOA including edge environment with data diode to access the data from the core process control zone without affecting the security and robustness of the DCS. Readymade apps based on #MechanisticAI so no software development is required, and non-intrusive WirelessHART sensors to easily install and integrate without coding or scripting. The key takeaway is that companies must assign a greater portion of their technology budget to the plant I&C team for the #automation to achieve this. Please connect to learn more: https://lnkd.in/gXv4jExP #DigitalTransformation #digitalization #IIoT

Reduce downtime and maintenance costs or improve product quality, and process efficiency by using the right AI application. By combining the modular and high-performance PLCnext Control with the extension module for machine learning and flexible I/Os, you can harness the power of AI for these critical applications. The module’s local data exchange and Edge TPU integration enhance efficiency and enable intelligent decision-making directly within the control system. Where is the combination best used? ➡️ Predictive maintenance: Real-time monitoring of machines and systems. Benefit: Early detection of deviations and potential failures, leading to reduced downtime and maintenance cost ➡️ Quality control: Detection of production errors. Minimization of rejects, improved product quality, and process efficiency ➡️ Energy efficiency:Optimization of energy consumption. Uncover potential energy savings in buildings or production facilities. Have you ever thought about using AI in these areas? #plcnext #iamplcnext #automation #hardware #industrialautomation

With the growth of AI, the industry must address the test and measurement requirements for engineers. Kevin Schultz, NI (National Instruments), Emerson Nitin Dahad, EE Times | Electronic Engineering Times #NationalInstruments #Emerson #TestAndMeasurement #DataAcquisition #AI #Engineering #mioDAQ #Automation #USB #MeasurementSolutions #Calibration #DeviceTesting https://lnkd.in/gsXmDQhg

🚀 Embrace the Future with #Automation #Intelligence (AI) Technologies SPC!🚀 In the dynamic landscape of modern manufacturing, the integration of automation systems is indispensable for enhancing productivity, reducing costs, and ensuring competitiveness. At AI Technologies SPC, we specialize in delivering comprehensive automation system design and integration solutions tailored to your unique needs. 🌟 #OurServices: - Automation System Design and Integration - Process Analysis and Optimization - Customized Automation Strategy Development - Control Architecture Design and Component Selection - Safety and Compliance Assurance 🔍 Why Choose AI Technologies SPC? - Expertise in Industry Standards: Adherence to ISO 9001, Lean Manufacturing Principles, Six Sigma, IEC 61511, ISA-95, ASTM E2500, ISA-88, ANSI/ISA-95.00.01, and IEC 61131. - Tailored Solutions: We develop automation strategies aligned with your organizational goals. - Cross-Functional Collaboration: Working closely with your teams to define objectives and success criteria. - Proven Methodologies: Employing rigorous analysis techniques and best practices. - Safety: Ensuring the safety and reliability of your systems. 🛠️ Specialized Support for Manufacturers: - Conducting thorough process analyses to identify inefficiencies and opportunities for automation. - Developing functional specifications and conducting risk assessments. - Selecting the best control hardware and software for seamless integration and performance. Automation system design and integration are strategic imperatives for modern manufacturing organizations seeking to remain competitive. By partnering with AI Technologies SPC, you can unlock the full potential of automation to streamline production, enhance efficiency, and drive sustainable growth. 🔗 Connect with us to transform your manufacturing processes with #cutting_edge automation #solutions! #Automation #Intelligence #ControlTechnologies #Manufacturing #LeanManufacturing #SixSigma #ISO9001 #IEC61511 #ISA95 #ASTME2500 #ISA88 #IndustrialAutomation #SmartManufacturing #AI #AItechnologies #ManufacturingExcellence

How to control the sensitivity of a controller? Controlling the sensitivity of a controller typically involves adjusting parameters or settings to modify its responsiveness to input signals or disturbances. The specific method for controlling sensitivity depends on the type of controller being used (e.g., proportional-integral-derivative (PID) controller, fuzzy logic controller, etc.) and the control system application. Here are some general strategies for controlling sensitivity: 1. Gain Tuning: Adjust the proportional, integral, and derivative gains (P, I, and D) of a PID controller. Increasing the proportional gain makes the controller more sensitive to error, while increasing the integral gain reduces steady-state error and improves system response to changes. The derivative gain helps dampen oscillations but can also increase sensitivity to noise. 2. Filtering: Apply filters to the input signals or feedback to reduce noise and disturbances. Filters such as low-pass filters can help smooth out noisy signals, reducing sensitivity to high-frequency disturbances. 3. Anti-windup Mechanisms: Implement anti-windup mechanisms in PID controllers to prevent integrator windup, which can occur when the controller output saturates. 4. Saturation Limits: Set limits on the controller output to prevent saturation, which occurs when the controller output reaches its maximum or minimum value. Saturation limits can help prevent excessive sensitivity to large disturbances or control signal fluctuations. 5. Adaptive Control: Use adaptive control techniques to automatically adjust controller parameters based on system conditions or performance metrics. Adaptive control algorithms can help maintain optimal sensitivity in varying operating conditions. 6. Robust Control: Design robust controllers that are less sensitive to variations in system parameters or external disturbances. Robust control techniques, such as H-infinity control or model predictive control, are designed to provide stable performance despite uncertainties in the system. 7. Fuzzy Logic Control: Adjust membership functions and fuzzy rules in fuzzy logic controllers to modify sensitivity to input variables. Fuzzy logic controllers offer flexibility in adjusting sensitivity based on linguistic variables and expert knowledge. 8. System Identification: Use system identification techniques to model the dynamics of the controlled system accurately. By understanding the system dynamics, controllers can be tuned more effectively to achieve the desired sensitivity and performance. 9. Performance Metrics: Define performance metrics such as rise time, settling time, overshoot, and steady-state error to evaluate controller sensitivity and performance. 10. Simulation and Testing: Use simulation tools and real-world testing to evaluate controller performance under different operating conditions and disturbances.