Review of Fault Detection and Diagnosis Techniques for AC Motor Drives

Abstract

Condition monitoring in electric motor drives is essential for operation continuity. This article provides a review of fault detection and diagnosis (FDD) methods for electric motor drives. It first covers various types of faults, their mechanisms, and approaches to detect and diagnose them. The article categorizes faults into machine faults, power electronics (PE) faults, DC link capacitor faults, and sensor faults, and discusses FDD methods. FDD methods for machines are categorized as statistical methods, machine-learning methods, and deep-learning methods. PE FDD methods are divided into logic-based, residual-based, and controller-aided methods. DC link capacitor and sensor faults are briefly explained. Machine and PE faults are listed and presented as tables for easy comparison and fast referencing. Most papers are selected from the past five years but older references are added when necessary. Finally, a discussion section is added to reflect on current trends and possible future research areas.

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

“…Over the years, several methods for diagnosing IM stator failure have been developed [7][8][9][10][11]. These methods are based on various types of diagnostic signals, such as input voltage [12], stator phase current [13], temperature [14], active and reactive power [15], vibration [16], and axial flux [17].…”

A Diagnostic Curve for Online Fault Detection in AC Drives

“…A lot of scientific papers are concerned with this matter [13,14]. Modern review articles [15,16] that cover the existing methods of AC drive diagnostics reveal several common trends in the development of diagnostics systems. These include the search for new means of measuring parameters [17,18], the search for algorithms for determining specific types of defects in electrical equipment [19,20], system approaches in solving diagnostic problems [21,22], and the use of artificial intelligence in diagnostic problems [23,24].…”

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

“…In a typical vibration-based condition monitoring system, multiple accelerometers are affixed to the machinery to capture vibration signals. These signals are subsequently processed to extract the informative features that enable precise fault localization 2 . However, employing vibration analysis necessitates direct contact with the machine for sensor attachment.…”