A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data

Abstract

Limited fault data restrict deep learning methods in solving fault diagnosis problems in rotating machinery. Using limited fault data to generate massive data with similar distributions is an important premise in applying deep learning methods to solve these problems. In this study, a new data generation approach is designed using a modified Wasserstein auto-encoder (MWAE) to generate data that are highly similar to the known data. The sliced Wasserstein distance is introduced to measure the distribution difference. A squeeze-and-excitation attention mechanism is developed for the encoder to extract more representative features from limited data. The sliced Wasserstein distance with a gradient penalty is designed as the regularization term to minimize the difference between the posterior distribution QZ obtained by the encoder and the predefined Gaussian prior distribution PZ. A dataset of simulated high-speed aeronautical bearings is used to illustrate the data generation ability of the proposed method; a comparison with results obtained using other methods confirms its excellent data generation ability.