Revolutionizing motor maintenance: a comprehensive survey of state-of-the-art fault detection in three-phase induction motors

Abstract

This comprehensive review delves into electrical machine fault diagnosis techniques, with a particular emphasis on three-phase induction motors. It covers a variety of faults, including eccentricity, broken rotor bars, and bearing faults. It also covers techniques like motor current signature analysis (MCSA), partial discharge testing, and artificial intelligence (AI)-based approaches. This review focuses on fault diagnosis techniques for electrical machines, specifically eccentricity faults, squirrel cage rotor faults, and bearing faults. It discusses their efficacy, applications, and limitations, as well as the role of AI and neural network techniques in modern fault detection applications. The review covers not only eccentricity faults, but also stator or armature faults caused by insulation failure, as well as bearing faults classified as ball, train, outer, and inner races. It focuses on early detection to ensure optimal machine performance and reliability, while also providing insights into fault detection mechanisms. Modern ways of finding problems with machines, like non-negative matrix factorization, rectified stator current analysis, incremental broad learning, and AI-based methods, make machines work better and stop money from being lost. The review is a valuable resource for practitioners and researchers in the field, allowing them to make better decisions about maintenance strategies and increase machine efficiency.