Fault diagnosis and investigation techniques for induction motor
Abdelelah Almounajjed, Ashwin Kumar Sahoo, Mani Kant Kumar, T. Assaf
Abstract
Induction motors are the most popular machines in industrial drive and power conversion systems. This popularity makes the recent industries impose reliable and continuous work for such motors. Detection of the incipient machine fault and estimation of the failure severity is a significant issue in modern industrial plants. This work presents novel classification criteria based on the most crucial aspects of fault diagnosis schemes. Pertaining to classification of machine faults, the suggested criteria are based on various signal processing tools as well as artificial intelligence methods. The proposed classification criteria allow capturing the different strategies used in the fault detection operations easily. In addition, a comprehensive detailed study of the recent articles, reported in this field, is introduced in order to provide a clear idea about the last trends in the fault diagnosis domain. Furthermore, a thorough list of inferences, research gaps, limitations, and future trends is added pertaining to diagnosing various faults inside the induction machine.