The Role of Fault Detection and Diagnosis in Induction Motors

Abstract

Induction Motors (I-M) aim to enhance interface technologies for more safety, reliability, productivity, and greener operations. In addition, malfunction monitoring functionalities are embedded into the system to detect impending faults and predict their consequence on the system's future actions using fault diagnosis techniques. This article broadens the scope of their investigation on current trends in fault detection and diagnosis of induction motors (CT-FDD) of I-M. In this direction, Modern applications, in particular, depend heavily on the rapid and accurate diagnosis of machinery malfunctions, which leads to increased productivity and reduced downtimes. It is worth mentioning that Artificial Intelligence (A-I) is a powerful technique for enhancing the capacity of I-M fault diagnosis, notably during the upkeep decision-making process. With this aim in mind, this article highlights the signatures of failure, including analysis of current motor signatures, voltage signatures, and acoustic and vibration analysis. In this direction, Actual stages in the design process of a fully automated CT-FDD system, such as system information processing, data capture, information theory, fault classification, and repair selection actions, are explained by the variation to provide an overview of the current state of CT-FDD.