AI-Enhanced IoT Systems for Smart Predictive Maintenance in Steel Plants

This paper introduces a smart Internet of Things (IoT) system that uses artificial intelligence (AI) to improve operational efficiency and reduce downtime in steel plants through predictive maintenance. It addresses equipment failures in steel plants with deployment of an AI-driven IoT system using neural networks for facilitating accurate defect prediction and minimizing maintenance expenses. The system uses neural networks for continuous operation and proactive maintenance, which are essential in steel production processes to provide productivity and safety. Early problem detection and health monitoring are made possible by integrating IoT devices for real-time data gathering and cloud computing for scalable data processing in the proposed system. Anomalies and impending breakdowns may be foreseen using neural networks trained on time-series sensor data, paving the way for predictive maintenance. The solution improves equipment dependability, optimizes maintenance schedules, and decreases operating costs using historical data and machine learning algorithms. Results from real-world applications show that the system may enhance maintenance plans while reducing downtime in steel manufacturing facilities. The results highlight how technology may revolutionize maintenance procedures, gain insight into proactive equipment management, and help the steel sector progress in smart production.