Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing

doi:10.1177/0020294019866858

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing
	Juan, Du 1; Yan, Lu 1; Xian, Tao 2; Yu, Zheng 3; Chu, Chen Guo 1
发表期刊	MEASUREMENT & CONTROL
ISSN	0020-2940
	2020-03-01
卷号	53 期号:3-4 页码:601-612
通讯作者	Yan, Lu(mly271515@163.com)
摘要	The main purpose of the paper is to propose a new method to achieve separating periodic impulse signal among multi-component mixture signal and its application to the fault detection of rolling bearing. In general, as local defects occur in a rotating machinery, the vibration signal always consists of periodic impulse components along with other components such as harmonic component and noise; impulse component reflects the condition of rolling bearing. However, different components of multi-component mixture signal may approximately have same center frequency and bandwidth coincides with each other that is difficult to disentangle by linear frequency-based filtering. In order to solve this problem, the author introduces a proposed method based on resonance-based sparse signal decomposition integrated with empirical mode decomposition and demodulation that can separate the impulse component from the signal, according to the different Q-factors of impulse component and harmonic component. Simulation and application examples have proved the effectiveness of the method to achieve fault detection of rolling bearing and signal preprocessing.
关键词	Resonance-based sparse signal decomposition Q-factor empirical mode decomposition energy operator demodulating fault detection
DOI	10.1177/0020294019866858
关键词[WOS]	EMPIRICAL MODE DECOMPOSITION ; DIAGNOSIS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61703399] ; Open Project Program of Shang hai Key Lab of Advanced Manufacturing Environment[KT20190602]
项目资助者	National Natural Science Foundation of China ; Open Project Program of Shang hai Key Lab of Advanced Manufacturing Environment
WOS研究方向	Automation & Control Systems ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Instruments & Instrumentation
WOS记录号	WOS:000537193300029
出版者	SAGE PUBLICATIONS LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39635
专题	中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Yan, Lu
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 3.Shang Hai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Juan, Du,Yan, Lu,Xian, Tao,et al. Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing[J]. MEASUREMENT & CONTROL,2020,53(3-4):601-612.
APA	Juan, Du,Yan, Lu,Xian, Tao,Yu, Zheng,&Chu, Chen Guo.(2020).Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing.MEASUREMENT & CONTROL,53(3-4),601-612.
MLA	Juan, Du,et al."Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing".MEASUREMENT & CONTROL 53.3-4(2020):601-612.