Mechanical compound fault diagnosis via suppressing intra-class dispersions: A deep progressive shrinkage perspective

Abstract

Compound faults and their involved single faults often have severe overlap in traditional feature spaces, and the strong background noise unavoidably exacerbates the degree of overlap. Aiming at the problem, this article constructs a multi-level discriminative feature learning method, namely deep progressive shrinkage learning, to progressively suppress intra-class dispersions using a few feature-level shrinkage modules and a decision-level shrinkage module for separating compound faults from single faults. First, soft thresholding is embedded as a key part of feature-level shrinkage modules to gradually eliminate noise-related information in the multi-layer feature learning process, in which thresholds are adaptively set using attention mechanism. Second, in the decision-level shrinkage module, high penalties are imposed on the samples that are far from their class centers. Finally, the efficacy of the method in compound fault diagnosis along with single faults has been verified through a variety of experiments.