Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.

Condition Monitoring and Fault Diagnosis of Electrical Motors&#8212;A Review

Research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus other signals such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. Human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems to name a few. Keeping in mind the need for future research, this review paper describes different types of faults and the signatures they generate and their diagnostics' schemes.

Condition monitoring and fault diagnosis of electrical machines-a review

Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of testing and monitoring methods, categorizing them into online and offline methods, each of which is further grouped into specific areas according to their physical nature. The main focus of this paper is on testing and monitoring techniques that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. In order to give a compact overview, the results are summarized in two tables. In addition to monitoring methods on turn-to-turn insulation, some of the most common methods to assess the stator's phase-to-ground and phase-to-phase insulation conditions are included in the tables as well.

A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

This paper describes the use of the extended Park's vector approach (EPVA) for diagnosing the occurrence of stator winding faults in operating three-phase synchronous and asynchronous motors. The major theoretical principles related with the EPVA are presented and it is shown how stator winding faults can be effectively diagnosed by the use of this noninvasive approach. Experimental results, obtained in the laboratory, corroborate that these faults can be detected, in the EPVA signature, by the identification of a spectral component at twice the fundamental supply frequency. On-site tests, conducted in a power generation plant and in a cement mill, demonstrate the effectiveness of the EPVA in the detection of stator circuit faults in large industrial motors, rated up to 5 MW.

Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach

Turn fault detection is based on the principal that symmetrical (unfaulted) motors powered by symmetrical multiphase voltage sources will have no negative sequence currents flowing in the leads. A turn-to-turn fault will break that symmetry and give rise to a negative sequence current which may then be used as a measure of fault severity or to initiate protective action such as a circuit breaker trip. A new way of looking at the effects of turn faults has been developed that improves sensitivity and speed while reducing the probability of misdetection, taking into account voltage balance, load or voltage variation and instrument errors. The method has been implemented on a PC and tested, in real time, on a specially prepared small motor. Reliable detection of one shorted turn out of 648 turns per phase (in a Y connected motor) was demonstrated with the fault indicator becoming fully developed in two cycles of line frequency after initiation of the fault.

A new approach to on-line turn fault detection in AC motors

Turn-to-turn fault detection is based on the principle that symmetrical (unfaulted) motors powered by symmetrical multiphase voltage sources will have no negative sequence currents flowing in the leads. A turn-to-turn fault will break that symmetry and give rise to a negative sequence current which may then be used as a measure of fault severity or to initiate protective action, such as a circuit breaker trip. A new way of looking at the effects of turn faults has been developed that improves sensitivity andspeedwhile reducing the probability of misdetection, taking into account voltage balance, load or voltage variation, and instrument errors. The method has been implemented on a PC and tested, in real time, with controllable faults in a small motor. Reliable detection of one shorted turn out of 648 turns per phase (in a Y-connected motor) was demonstrated with the fault indicator becoming fully developed in two cycles of line frequency after initiation of the fault.

D. Hoeweler

Papers

A new approach to on-line turn fault detection in AC motors

Sensitive, On-Line Turn-to-Turn Fault Detection in AC Motors