Multi-objective adaptive guided differential evaluation blind deconvolution and its application in bearing fault detection

Abstract

Blind deconvolution (BD) methods applied to bearing fault detection often cause inferior performance due to inaccurate input parameters. Moreover, the optimal parameters of BD vary for different speeds and fault types of bearings, which seriously undermines the applicability of BD in practical industries. In this scenario, this paper proposes a parameter-adaptive BD method (MOBD) based on the multi-objective adaptive guided differential evaluation algorithm (MOAGDE). Firstly, based on the linear discriminant analysis, the quotient of inter-class distance and intra-class distance is used to determine the superiority of common bearing fault characteristic indicators to establish the multi-objective function of MOAGDE. Then, the optimal parameters of BD are searched by MOAGDE improved by dynamic switched crowding method (DSC-MOAGDE). Finally, the bearing is judged whether or what kind of fault has occurred according to the fault type locating index proposed in this paper. The main advantage of MOBD is that only bearing speed and type priories are required to achieve online detection of bearing faults. The results of simulation and experimental signals demonstrate that MOBD significantly outperforms the traditional BD method.