① 汽车运输安全保障技术交通行业重点实验室，长安大学汽车学院，陕西西安，710064
② 西安格蒂电力有限公司，陕西西安，710075
An Universal Particle Swarm Optimized Independent Component Analysis Algorithm
Li Liangmin, Ren Jingyan
① Key Laboratory of Automotive Transportation Safety Enhancement Technology of the Ministry of Communication, School of Automobile, Chang’an University, Xi'an 710064, China
② Grid Electric Power Science & Technology Co., Ltd., Xi’an 710075, China
Two sets of synthetic signals are created to test the separation ability of three popular independent component analysis algorithms—JADE, FastICA, and extended-Infomax. The conclusion is drawn that the above independent component analysis can’t recover source signals from the mixtures of super-Gaussian and sub-Gaussian precisely, and FastICA fails in solving the separation problem of strong sources mixed with weak sources. A particle swarm optimized independent component analysis algorithm, which chooses the difference between joint probabilities and products of marginal probabilities of separated signals as the objective function, is proposed. The implementation procedure is described in detail. Simulation tests show that, compared with the above three independent component analysis algorithms, the proposed algorithm performs the best. Furthermore, the implementation of the proposed algorithm relies on no prior knowledge, thus more suits for solving practical engineering problem. Finally, the proposed algorithm is used to process sound signals sampled from a rolling bearing test rig. Analysis of the separated signal reveals the cause of bearing failure, indicating the validity of the proposed algorithm.
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