Edge AI-Driven Sensor Data Analysis for Electric Motor Health Monitoring and Predictive Maintenance

Abstract

Electric motors are the backbone of industrial productivity, safety, and energy efficiency, making their reliable operation essential to modern manufacturing. As Industry 4.0 reshapes how equipment is managed, predictive maintenance has emerged as a critical capability. Yet traditional condition monitoring still depends largely on routine servicing, periodic inspections, centralised processing, and predefined thresholds – approaches that limit early fault detection and offer little support for real-time monitoring. This research proposes an AI-driven, edge-based system for electric motor health monitoring and predictive maintenance using multi-sensor data. The system integrates vibration, temperature, and electrical current sensors for continuous data acquisition, and a deep learning classifier was developed to predict motor health and detect faults at an early stage. To achieve low latency, reduced bandwidth usage, and fast on-site decision-making, the model was optimised and deployed using TinyML, an edge AI framework. A companion web application supports remote monitoring, real-time visualisation, and maintenance alerts. Using an industrial dataset of 8,000 records, the TinyML model was trained, optimised, and deployed on an Espressif ESP32 microcontroller. Through spectral analysis and 8-bit quantisation, the system achieved a classification accuracy of 92.3% with an inference latency of just 16 ms, while consuming only 13.4 KB of RAM and 95 KB of Flash memory. These results demonstrate that high-speed, decentralised predictive maintenance is feasible on low-power hardware, offering a scalable solution for the energy and manufacturing sectors, reducing unplanned downtime and enabling rapid response to abnormal operating conditions.