AI-Driven Predictive Maintenance for Industrial IoT with Real-Time Fault Detection and Prediction

Predictive maintenance for industrial machinery uses data analysis and advanced techniques to forecast equipment failures. This proactive approach schedules maintenance to minimize downtime and optimize operational efficiency. Achieving accurate, consistent, and well-integrated IoT sensor data is difficult, as poor data quality can result in inaccurate predictions and false alerts. Scaling real-time fault detection and prediction requires substantial computational power and optimized algorithms to process the continuous influx of data from multiple machines. To address data quality and integration issues while enhancing real-time processing and scalability, a Custom Neural Network technique can be used to improve IoT sensor data accuracy and consistency, and efficiently manage the computational demands of continuous data influx for fault detection and prediction. The proposed system, leveraging an extended Neural Network technique, enhances prediction accuracy by efficiently managing diverse industrial datasets and improves real-time fault detection through optimized data processing and reduced latency. The model demonstrated a remarkable accuracy of 92.86%, showcasing its ability to accurately identify both faulty and non-faulty equipment. With a precision of 90.91%, a perfect recall of 100%, and an F1 score of 95.24%, the model exhibited a well-balanced fault detection performance, minimizing false positives while ensuring thorough fault identification, all while processing in real-time with an efficient time complexity of 2.49 seconds.