Considering the advantages of non-contact and convenient acquisition of sound signals,this paper proposes a belief rule base(BRB)-based fault diagnosis method for the sound signals of automotive mirror system.Firstly,multiscale dispersion entropy(MDE)is used to extract the features of the sound signals;then,the extracted features and the expert's empirical knowledge are fused to establish a belief rule base fault diagnosis model;finally,the projection covariance matrix adaptive evolutionary strategy(P-CMA-ES)is used to optimize the initial parameters given by the experts in the BRB to improve the accuracy of the model.Finally,the effectiveness and accuracy of the proposed method are verified using the sound signal monitoring data using the endurance test of a certain type of automobile sight glass system.
维普
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