IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction - Mr. Gaurav B. Landage; Prof. Pragati B. Chandane; Prof. Jagruti R. Mahajan; Dr. Hemant Kumar B. Jadhav; Dr. Pradeep M. Patil (International Journal of Electrical Machine Analysis and Design, 2026)
Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things (IoT) technology with machine learning (ML) for early defect prediction and real-time condition monitoring in order to get around these restrictions. Using embedded sensors mounted on the motor, the suggested system continuously gathers operating information like temperature, vibration, current, voltage, and rotational speed. An Internet of Things communication infrastructure is used to deliver this sensor information to These sensor readings are transmitted through an IoT communication framework to a cloud-based or edge-processing platform, where the data is stored, processed, and analyzed. Feature extraction techniques are applied to transform raw sensor data into meaningful indicators that reflect the motor’s health condition. Machine learning algorithms are then trained on historical and real-time datasets to identify abnormal patterns, classify fault types, and predict potential failures before they occur, boosting reliability, cutting expenses, and improving the performance of the entire system.
Keywords: Fault diagnosis; Internet of things (IoT); Machine Learning; Motor Health Monitoring; Predictive Maintenance; Smart Maintenance; condition monitoring; industrial automation