Acta Metrologica Sinica  2019, Vol. 40 Issue (5): 855-860    DOI: 10.3969/j.issn.1000-1158.2019.05.19
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Fault Diagnosis of Rolling Bearing Based on Sparse Representation of Signals and Transient Impulse Signal Multifeature Extraction
MENG Zong,YIN Na,LI Jing
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
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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 wordsmetrology      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
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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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