Computer Science ›› 2016, Vol. 43 ›› Issue (12): 302-306.doi: 10.11896/j.issn.1002-137X.2016.12.056

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Investigation on Fault Classification Method of K-PSO Sparse Representation

FU Meng-meng and WANG Pei-liang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to solve the problem of multiple faults which can not be identified and classified accurately in modern complex production process,an improved sparse representation fault classification method was proposed.This method is based on the sparse representation of the signal to determine the fault categories.First,the specific implementation process utilizes K-Means Singular Value De-composition(K-SVD) algorithm to constructe over complete dictionary with main features in the original message,and then uses the particle swarm optimization(PSO) algorithm to search and find the most matching atom which is generated in sparse decomposition in the range of over complete dictionary.Finally,the results based on the sparse representation realizes classification and identification about multiple faults problem.The validity and practicability of the proposed method is verified by numerical simulation.Meanwhile,the proposed method was compared with the methods based on the BP neural network and SVM classification through the fault classification of diesel engine fuel system.Experiments show that the algorithm has good effect on fault classification.

Key words: Sparse representation,K-SVD algorithm,Particle swarm optimization algorithm,Fault classification

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