基于信号稀疏表示和瞬态冲击信号多特征提取的滚动轴承故障诊断

doi:10.3969/j.issn.1000-1158.2019.05.19

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Abstract A multi-feature extraction method based on IChirplet atom is proposed to solve the problem of sparse representation and feature extraction of transient impact signal. According to the characteristics of the fault signal, a dictionary which is made up of IChirplet atoms is built and a improved pariticle swarm optimization algorithm is presented for searching the best atoms. And then sensitive parameters in time-frequency domain of the IChirplet atomics and characteristics of the reconstructed signal are extracted as the characteristic parameter. Finally, the fault classification is achieved by PSO_SVM. The experiment proves that the IChirplet atom can match the fault signal of rolling bearing well, and the multiple characteristics can better reflect fault information and the bearing failure type can be judged more accurately.

Key words： metrology rolling bearing fault diagnosis sparse decomposition IChirplet atoms multi-feature extraction

Received: 03 May 2018 Published: 01 September 2019

PACS:	TB936
	TB973

Corresponding Authors: Zong MENG E-mail: mzysu@ysu.edu.cn

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	MENG Zong
	YIN Na
	LI Jing

Cite this article:

MENG Zong,YIN Na,LI Jing. Fault Diagnosis of Rolling Bearing Based on Sparse Representation of Signals and Transient Impulse Signal Multifeature Extraction[J]. Acta Metrologica Sinica, 2019, 40(5): 855-860.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.05.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I5/855

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