32 n CONTROLS AND SOFTWARE November/December 2025 www.drivesncontrols.com Which AI should you use in control systems? As artificial intelligence is adopted increasingly into manufacturing and industrial processes, many wonder where to apply AI in their systems. Machine learning and neural networks are two tools that can transform control systems – but they serve different roles depending on the nature of your data. Any numerical data that can be extracted from analogue input terminals is a gold mine for a machine-learning algorithm. The system can analyse data such as pressure, temperature, voltage, current or vibration, and provide an output that can be interpreted by other systems. For instance, in a wind turbine, two chief components to monitor are the shaft and bearing. Here, temperature and vibration values can be fed into a machine-learning algorithm which will then decide whether to take action, such as deploying the brakes, to protect the turbine. Shafts and bearings are found in many sectors, in applications such as the mixers, blenders and packaging machines found in the food and drink sector, or tablet presses, centrifuges and filling lines in pharmaceutical manufacturing. In industries such as aerospace, food and drink or pharma, where minor defects can have serious consequences if they escape detection, a facility might need more than the machine learning system mentioned previously. A neural network makes use of more complex data, such as images or live audio/video feeds from industrial cameras. Consider a biscuit production line. A neural network can perform two different tasks on a visual feed of the line’s output: recognition and classification. First, the neural network examines an image and considers whether the image shows a biscuit, or if an anomaly such as a foreign object or contaminant has entered the production line. If it is looking at a biscuit, the neural network can then classify the image. It can ask a range of questions, such as: Is this biscuit cracked or broken? or Is there a label out of place or missing? If so, the network can classify that biscuit as “non-OK”, while yesses would generate an “OK” classification. Using machine learning, it’s possible to monitor external assets and diagnose issues remotely. Once a problem is diagnosed, operators can plan for downtime, deploy maintenance engineers to make repairs or replacements and then resume manufacturing. This proactive maintenance is less disruptive than reactive fault-finding and repairs after a component has failed. In some manufacturing processes, such as the enclosure of anchor bolt sleeves, small deviations in quality can be difficult to catch without manual inspection. If these inconsistencies make it to the end-user, they can result in complaints or product failure. By integrating a neural network directly into the control system, manufacturers can perform real-time quality checks using existing machine data. This not only reduces defects and waste but also ensures consistently high output without the need for additional sensors or test stations. Engineers need to understand their process data to decide whether machine learning or neural networks offer the most effective solution for an application. n Machine learning and neural networks can both be used to improve industrial operations – but how do you decide which is best for a particular application? Ivalyo Ivanov, a support engineer with Beckhoff UK, compares these two powerful tools in the AI toolbox.
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