Feature extraction of induction motor stator fault based on particle swarm optimization an

Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet

packet

WANG Pan-pan;SHI Li-ping;HU Yong-jun;MIAO Chang-xin

【期刊名称】《煤炭学报：英文版》 【年(卷),期】2012(018)004

【摘要】To effectively extract the interturn short circuit fault features of induction motor from stator current signal,a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed.First,according to the maximum inner product between the current signal and the cosine basis functions,this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO,which can eliminate the fundamental component and not affect other harmonic components.Then,the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor.Finally,the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method. 【总页数】6页(432-437) 【关键词】

【作者】WANG Pan-pan;SHI Li-ping;HU Yong-jun;MIAO Chang-xin 【作者单位】School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China;School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China;School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China;School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China 【正文语种】中文 【中图分类】 【文献来源】

https://www.zhangqiaokeyan.com/academic-journal-cn_journal-coal-english_thesis/0201216989485.html 【相关文献】

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