IoT-Enabled Predictive Maintenance system for Industrial Machines Using Machine Learning

Unplanned downtime in critical industrial infrastructure contributes approximately 50 billion in annual global losses. Conventional maintenance paradigms—predominantly reactive or schedule-based—fail to detect early-stage degradation, leading to premature component replacement or catastrophic failure. This research presents an IoT-enabled Predictive Maintenance (PdM) framework utilizing a six-layer IIoT architecture to process high-frequency vibration, temperature, and current data. Integrating Edge-AI for low-latency anomaly detection with cloud-based deep learning for Remaining Useful Life (RUL) estimation, the system provides a holistic view of equipment health. Random Forest and hybrid CNN-LSTM models were implemented to capture complex temporal dependencies in sensor streams. Experimental validation over five months in a steel wheel manufacturing facility demonstrates a fault classification accuracy of 99. 03%, a 75% reduction in unplanned downtime, and an 80% decrease in failure frequency. This study confirms that the IoT–ML synergy facilitates a critical transition toward proactive maintenance in Industry 4. 0 environments.