Multi-period synchronous deconvolution for complex fault diagnosis of rolling element bearings

Due to inaccurate period knowledge or incomplete information, there are limitations in extracting all potential complex faults of rolling bearings and enhancing weak faults. To address this problem, this article introduces a novel deconvolution method, named multi-period synchronous deconvolution (MPSD). In this method, decomposing matrix-matrix in solving the filter coefficients are used to replace designing objective function to avoid only obtaining the filter signal of the optimal objective function. Based on eliminating the effects of subspace noise, a novel evaluation index, called characteristic information ratio, is proposed to evaluate fault significance by fault information levels instead of energy. In addition, an informative subspace selection strategy is proposed to control the weighting coefficients of each subspace in reconstructed signal. Without the predetermined fault periods, the proposed MPSD can simultaneously extract latent multiple faults and enhance weak fault features. Finally, simulations and experimental cases substantiate the efficacy and eminence of MPSD.