EV Starter Motor Fault Detection Using DWT Skewness and Kurtosis 核心 · 已核验

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"The present paper proposes a new diagnostic technique for determining short circuit conditions in automobile starter motor armature coils by computing the skewness and kurtosis of current waveforms filtered by wavelet transform. Using MATLAB Simulink, we developed a model to simulate an automobile system with incremental inter-turn errors in the starter motor, enabling the acquisition and analysis of signals at different fault stages using discrete wavelet transform (DWT). This method diagnoses faults early in the transient anomalies by analyzing signals in different resolutions. The skewness and kurtosis of wavelet coefficients, essential for fault diagnosis, provide critical information for distinguishing normal, pre-fault, and fault states. This new diagnostic approach enhances the precision of problem identification, improving safety and dependability in automotive repair. "

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落地页

https://ieee-dataport.org/documents/ev-starter-motor-fault-detection-using-dwt-skewness-and-kurtosis

国内可访问性

国内直连:可达 (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
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发布年份

2025

发布方

IEEE DataPort

设备类型

dc_motor

PHM 任务

fault_detection

分发点

故障工况

传感器