Abstract Unexpected malfunctions in industrial rotating machinery frequently result in decreased output and higher operating expenses. To achieve real-time anomaly identification and remaining useful life estimation, this work presents a novel predictive maintenance architecture that combines IoT-based multi-sensor, edge inference, and an interpretable AI model. For thorough data collection, the pro posed system uses an ESP32 board interfaced with the various sensors. The edge computing node is a Raspberry Pi 5, which runs the proposed hybrid Random Forest algorithm for remaining useful life prediction and fault categorisation. With a mean absolute error of 1.58 h, an inference latency of 38 ms per cycle, and an accuracy of 99.0%, F1-Score of 99.3%, and Remaining Useful Life (RUL) prediction with R2 = 0.9995, trained and evaluated on 1200 samples collected from a single induction motor testbed. Email notifications, IoT-based alerting via Blynk, and real-time visualization via a Tkinter dashboard interface prompt a response to crucial circumstances. Decision trans parency was achieved through the use of SHapley Additive exPlanations (SHAP) attribution analysis. The results suggest suitability for Industry 4.0 deployment in small enterprises.
Patel et al. (2026) studied this question.