Independent Component Analysis Method Based on Genetic Algorithm in Compound Fault Separation

Abstract

Compound faults often exist in roller bearing, which increase the difficulty for the fault diagnosis. In order to extract the characteristics of compound fault signals, independent component analysis (ICA) method was used to
separate fault signals. However, the selection of initial weight about ICA will affect the number of iterations and astringency. Therefore, a novel ICA method based on improved genetic algorithm was proposed. The kurtosis of
signal was used as the optimization function, and then genetic algorithm was applied to find the separation matrix according to the maximum of the kurtosis. This method avoids the problem of redundant iteration and convergence, which is caused by randomness of initial weight vector. Finally, the proposed method was used to separate source signal from the mixed signals and achieve the roller bearings fault identification and separation. The results show that the proposed method is superior to traditional ICA method, and the compound fault can be separated based on the proposed method.