Manifold learning-assisted sparse filtering method for machinery fault diagnosis

Abstract

Sparse filtering (SF) has received considerable attentions in the machinery fault diagnosis thanks to its ability to extract the fault-related features using their sparsity. However, the existing SF methods have dilemmas with the empirical selection of model parameters, the loss of fault-related information caused by a screening way for the target mode, and the singularity of results induced by some large-amplitude random impulses (LARIs). Hence, a manifold learning-assisted SF method is proposed for machinery fault-related feature enhancement in this study. First, an improved intrinsic component filtering (ICF) is presented for extracting the multiple modes with feature enhancement, where the parameters of ICF are adaptively determined by using the optimization object to avoid the empirical selection of parameters. Second, the manifold learning is introduced to compress the enhanced multiple modes to overcome the loss of fault-related information; thus the intrinsic manifolds are obtained for disclosing the buried fault-related features and suppressing the band-in noise. Third, an adaptively weighting strategy for these intrinsic manifolds is constructed to obtain a final representative mode for conducting the machinery fault diagnosis. Meanwhile, the LARIs coupled with intrinsic manifolds are calibrated according to their statistical information to resolve the singularity of the representative features. Simulation and experiments show that the proposed method is more effective in extracting fault-related features than some existing methods.