Predictive maintenance of induction motors using machine learning: A random forest based fault prediction approach

Predictive maintenance has emerged as a critical approach for improving reliability and availability of industrial and electrical equipment. Traditional maintenance strategies often lead to unexpected failures, increased downtime, and high operational costs. With the advancement of machine learning techniques, data-driven predictive maintenance has become an effective solution for early fault detection and failure prediction. This paper presents a comprehensive review of machine learning based predictive maintenance techniques, focusing on commonly used algorithms such as Random Forest, Support Vector Machine (SVM), and XGBoost. The review discusses sensor data utilization, preprocessing techniques, model performance, applications in industry and defence systems, challenges, and future research directions.