Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

IF 1.6 Q4 ENERGY & FUELS
Adil Usman, Bharat Singh Rajpurohit
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Abstract

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (vs) and current (is) signals and machine signatures based on motor back electromotive force (EMF) (EB) and magnetic flux (ϕ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

Abstract Image

用于全电动交通系统的永磁交流电机的状态监测:技术现状
本文介绍了用于电动汽车(EV)应用中永磁交流(AC)电机状态监测的新兴高效技术的最新发展状况。讨论内容包括永磁电机最常见和最特殊的故障类型,如转子退磁和定子匝间故障。故障指标,如电压(vs)和电流(is)信号,以及基于电机反向电动势(EMF)(EB)和磁通量(j)的机器特征,都被视为诊断电机故障的测量量。其他特征,包括热分析、声学噪声和振动,也作为一些新兴技术在估算电动汽车电机运行时的性能时进行了说明。此外,还介绍了各种故障建模方法、状态监测技术以及在机器故障萌芽阶段诊断其影响的综合方法。由于本文讨论的大多数故障诊断技术都只将基于机器的量作为故障指数/指标,因此发现所提供的解决方案在诊断电机故障方面更加可靠和准确。除了作者提出的新方法外,本综合评论研究还包括目前在工业和商业实践中采用的现有故障诊断技术。因此,所有给定的状态监测方案对于估计全电动交通系统中运行中的永磁交流电机的健康状况似乎都非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
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