A universal particle swarm-optimized independent component analysis algorithm

被引：0

作者：

Li, Liang-Min ^{[1
]}

Ren, Jing-Yan ^{[2
]}

机构：

[1] Key Laboratory of Automotive Transportation Safety Enhancement Technology, Ministry of Communication, School of Automobile, Chang'an University, Xi'an

[2] Grid Electric Power Science & Technology Co., Ltd., Xi'an

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2015年 / 34卷 / 08期

关键词：

Extended-Infomax; FastICA; Independent component analysis (ICA); JADE; Particle swarm optimization (PSO); Rolling bearing;

D O I：

10.13465/j.cnki.jvs.2015.08.002

中图分类号：

学科分类号：

摘要：

Two sets of simulated signals were made to test the separation ability of three popular independent component analysis (ICA) algorithms including JADE, FastICA, and extended-Infomax. The results showed that the three ICA algorithms can't recover source signals from mixtures of super-Gaussian sources and sub-Gaussian ones precisely; FastICA fails in solving the separation problem of strong sources mixed with weak sources. A particle swarm optimized ICA algorithm minimizing the difference between joint probabilities and products of marginal probabilities of separated signals was proposed. The computing procedure was derived. Simulation tests showed that compared with the above three ICA algorithms, the proposed algorithm is the best; furthermore, the implementation of the proposed algorithm needs no prior knowledge, thus it is more suitable for solving practical engineering problems. Finally, the proposed algorithm was used to process the actual signals sampled from a rolling bearing test rig. The separated signals were analyzed to indentify the fault types of rolling bearings, the effectiveness of the proposed algorithm was verified. ©, 2015, Chinese Vibration Engineering Society. All right reserved.

引用

页码：7 / 11and25

页数：1118

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