Predicting anomalies with AI

In industrial environments, the sudden stoppage of essential machinery can lead to significant issues such as delays in order processing, production of defective parts, and contractual penalties. These problems worsen when spare parts are difficult to procure, and service interventions are delayed, sometimes extending from hours to days.

Maintenance approaches

To mitigate such scenarios, companies traditionally employ periodic inspections and replace components based on their expected service life. These proactive approaches are economically justified, as the costs associated with system failures typically surpass the expenses incurred by precautionary component replacements.

AI has revolutionised this process by providing unparalleled speed and efficiency in recognising anomalies from machine data streams. Algorithms based on artificial intelligence can detect early signs of potential failures, often hours or days before they occur. This early detection allows for planned interventions, minimising unplanned downtime and ensuring continuity in production.

AI-based predictive maintenance systems

AI-based predictive maintenance systems are trained on historical data to identify patterns associated with normal and abnormal machine behaviors. These systems act as effective early warning mechanisms, often detecting anomalies before they become apparent to human operators. When integrated into machine controllers or Human Machine Interfaces (HMIs), AI can directly communicate warnings to operators. This enables timely and planned machine shutdowns for necessary maintenance during less disruptive times, such as night breaks.

Mitsubishi Electric’s MELSOFT MaiLab is an example of an advanced solution that leverages AI for anomaly detection. This software reduces the computational power requirements traditionally associated with AI data analysis and accelerates data processing, facilitating its integration into industrial Internet of Things (IIoT) environments. By eliminating the need for initial data labeling and employing deep learning principles, MELSOFT MaiLab enhances the efficiency of AI in predicting machine anomalies.

In summary, predicting anomalies with AI not only enhances the reliability and efficiency of industrial operations but also provides a strategic advantage by reducing unexpected downtimes and associated costs. The integration of AI in anomaly detection marks a significant advancement in industrial maintenance practices, ensuring smoother and more efficient production processes.

Learn more about predicting anomalies with AI from our Empowering solutions: AI integrated into expert systems whitepaper: https://emea.mitsubishielectric.com/fa/lp/artificial-intelligence-wp?