Deep Sparse Topology Network for Robust Bearing Fault Diagnosis by Maximizing Prior Knowledge Functions

Abstract

Recent intelligent fault diagnosis of rotating machinery focuses on extracting inherent features using the powerful deep learning. Although the deep learning methods are efficient, they ignore maximizing functions of the available working condition and health condition prior knowledge. Hence, a novel diagnosis method referred to as deep sparse topology network (DSTN) is proposed to extract highly regular and fault-sensitive features. To maximize the health condition label function in fault-sensitive feature extraction, a regular and sparse feature topology is developed through a parameterized sparse label matrix. Along with topology optimization, working condition labels are used utilizing the high-order Kullback–Leibler divergence, to enhance feature robustness to working condition variation and noise. Finally, with these highly regular and fault-sensitive features, the proposed DSTN directly points out the predicted health condition labels. To the best of our knowledge, the proposed network is the first attempt to directly diagnose faults through highly regular fault-sensitive features. The mechanism analysis and experimental investigations validate the efficiency and robustness of DSTN.