Intelligent Fault Detection in Inverter-Fed Motors Using Machine Learning Techniques

Abstract

Inverter-fed motors are widely used in industrial applications due to their efficiency, controllability, and energy-saving capabilities. However, these motors are prone to various faults such as bearing failures, stator winding defects, rotor imbalances, and inverter-induced stresses, which can lead to reduced performance and unexpected breakdowns. Traditional fault detection methods rely on manual inspection or signal-based analysis, which are often time-consuming and less effective in handling complex and dynamic operating conditions. This paper presents an overview of machine learning techniques for monitoring and fault detection in inverter-fed motors.The proposed approach utilizes machine learning algorithms to analyze motor condition data such as vibration signals, current, voltage, and temperature. Techniques including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and deep learning models are employed to identify patterns and detect faults at early stages. These models enable automated, accurate, and real-time fault diagnosis, reducing maintenance costs and improving system reliability. The study highlights the advantages of machine learning in handling nonlinear relationships and large datasets, making it a powerful tool for predictive maintenance and intelligent monitoring of inverter-fed motor systems.