文献详情
A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition
作者Yi, Cancan[1];Lv, Yong[2];Dang, Zhang[3]
文献类型期刊
机构
通讯作者Lv, Y (reprint author), Wuhan Univ Sci & Technol, Sch Mech Engn, Wuhan 430081, Peoples R China.
期刊名称SHOCK AND VIBRATION影响因子和分区
2016
2016
增刊正刊
收录情况SCI(E)(WOS:000378708400001)  EI(20162702566553)  
期刊等级B
所属部门机械自动化学院
百度学术A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition
语言外文
ISSN1070-9622
DOI10.1155/2016/9372691
被引频次16
人气指数26
浏览次数26
基金National Natural Science Foundation of China [51475339, 51405353]; Key Laboratory of Metallurgical Equipment and Control of Education Ministry, Wuhan University of Science and Technology [2015B11]
摘要Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode's power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.
全部评论(0 条评论)
作者其他论文

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum.

Lv, Yong;Zhu, Qinglin;Yuan, Rui.SENSORS.2015,15(1),1182-1198.

Adaptive tensor morphology and its application in bearing failure diagnosis.

Wang, Long Sheng;Lv, Yong;Yuan, Rui.International Conference on Energy Equipment Science and Engineering, ICEESE 2015.2015,3,2323-2329.

Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing.

Lv, Yong;Yuan, Rui;Song, Gangbing.MECHANICAL SYSTEMS AND SIGNAL PROCESSING.2016,81,219-234.

Bearing fault diagnosis based on weighted phase space reconstruction.

Lv, Yong;Zhang, Hongwei;Li, Yourong,等.2010 International Conference on Digital Manufacturing and Automation, ICDMA 2010.2010,1,315-318.

登录