Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM-type BLDC motor

Adil Usman, Bharat Singh Rajpurohit
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引用次数: 1

Abstract

This study investigates partial demagnetization faults arising from stator interturn faults in a surface-mounted permanent-magnet-type brushless direct current motor. Because of rotor demagnetization, the fault severity increases significantly owing to an increase in the stator phase current and temperature. The effect of such a fault is reflected in machine parameters such as the motor back-EMF and radial magnetic flux, which are used to analyse the characteristics of faults. A mathematical model of a machine under possible fault conditions is developed using the finite element method and advanced hybrid model approaches. Experimental investigations are conducted to validate the proposed methodology. Subsequently, the machine parameters used for fault diagnosis are employed to develop an online expert-based system that can detect, classify and estimate the percent increase in the values of the parameters to determine the fault severity of the machine under fault conditions. It is discovered that the proposed approach is suitable for industrial and commercial applications in electric vehicles, where the machine's state-of-health estimation is crucial for avoiding major faults that may result in its failure.

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基于在线状态监测的spm型无刷直流电机部分退磁故障诊断系统的开发
研究了表面贴装永磁型无刷直流电机定子匝间故障引起的部分退磁故障。由于转子退磁,由于定子相电流和温度的增加,故障严重程度显著增加。这种故障的影响反映在电机反电动势和径向磁通等机器参数中,这些参数用于分析故障的特征。采用有限元法和先进的混合模型方法,建立了机械在可能故障条件下的数学模型。实验调查进行了验证所提出的方法。随后,利用用于故障诊断的机器参数,开发了一个基于专家的在线系统,该系统可以检测、分类和估计参数值的增加百分比,以确定故障条件下机器的故障严重程度。研究发现,该方法适用于电动汽车的工业和商业应用,其中机器的健康状态估计对于避免可能导致其故障的重大故障至关重要。
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