An Universal Particle Swarm Optimized Independent Component Analysis Algorithm

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (8) : 7-11.

Li Liangmin, Ren Jingyan

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Abstract

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.

Key words

Independent component analysis / FastICA / JADE / extended-Infomax / particle swarm optimization / rolling bearing

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Li Liangmin, Ren Jingyan. An Universal Particle Swarm Optimized Independent Component Analysis Algorithm[J]. Journal of Vibration and Shock, 2015, 34(8): 7-11

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