Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor

doi:10.1177/0020294019858181

	Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor
	Lu, Yan 1; Du, Juan 1; Tao, Xian 2
刊名	MEASUREMENT & CONTROL
	2019-09-01
卷号	52 期号:7-8 页码:1111-1121
关键词	Fault diagnosis resonance-based sparse signal decomposition optimal Q-factor genetic algorithm energy operator demodulating rolling bearing
ISSN号	0020-2940
DOI	10.1177/0020294019858181
通讯作者	Lu, Yan(mly271515@163.com)
英文摘要	When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor (Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.
资助项目	National Natural Science Foundation of China[61703399]
WOS研究方向	Automation & Control Systems ; Instruments & Instrumentation
语种	英语
出版者	SAGE PUBLICATIONS LTD
WOS记录号	WOS:000487111400037
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26995]
专题	中国科学院自动化研究所
通讯作者	Lu, Yan
作者单位	1.Shanghai Dianji Univ, Sch Elect Engn, Shanghai 200240, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Lu, Yan,Du, Juan,Tao, Xian. Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor[J]. MEASUREMENT & CONTROL,2019,52(7-8):1111-1121.
APA	Lu, Yan,Du, Juan,&Tao, Xian.(2019).Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor.MEASUREMENT & CONTROL,52(7-8),1111-1121.
MLA	Lu, Yan,et al."Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor".MEASUREMENT & CONTROL 52.7-8(2019):1111-1121.