Induction Motor Faults and Artificial Intelligence Based Conditioning and Monitoring Techniques

Abstract

Three phase induction motors have been intensively utilized in industrial applications, mainly due to their efficiency and reliability. These motors have good properties such as increased stability, robustness, durability, large power to weight ratio, low production costs and controllability easiness. All machines realize various stresses during operational conditions. These stresses might lead to some modes of failures or faults. Condition monitoring is necessary in order to prevent faults. These faults, are necessary to be identi?ed and categorized, as soon as possible as they can end up in serious damages if not detected in due time. Different techniques of fault monitoring for induction motors are broadly classified as techniques based on model, signal processing, and soft computing. In recent years the monitoring and fault detection of electrical machines have moved from traditional techniques to Artificial Intelligence (AI). In this paper an attempt has been made to review different faults on induction motors and the applications of neural/fuzzy artificial intelligence techniques for induction motor condition monitoring. A brief description of various AI techniques highlighting the merits and demerits of each has been discussed. The futuristic trends on condition monitoring of induction motors are also indicated.