Vibration-induced Thermal Fault Analysis and Optimisation of Induction Motors Using Artificial Intelligence

Induction motors play important roles in modern industrial operations due to their robustness, simplicity, cost-effectiveness and low maintenance. However, they are vulnerable to mechanical vibrations, which can reduce performance, generate excessive heating, and cause premature failure. This research explores the main sources of vibration such as bearing defects, mechanical imbalance, misalignment, rotor eccentricity, and environmental factors and their contribution to thermal faults in induction motors. The aim is to develop an intelligent diagnosis system capable of detecting thermal faults triggered by vibration using artificial intelligence. An autoencoder-based anomaly detection model was implemented to detect abnormal vibration patterns linked to overheating. Vibration data was sourced from an open-access industrial dataset. Preprocessing included data cleaning, feature scaling using min-max normalisation, and time-series reshaping using sliding windows. The dataset was divided into training and testing sets. Only healthy data was used for training, allowing the autoencoder to learn the standard operational behaviour of the motor. The model architecture included encoder and