Match-reinforcement learning with time frequency selection for bearing fault diagnosis

Abstract

Rolling bearings play a vital role in ensuring the safe operation of rotating machinery. However, in many application scenarios, the collected data has a low signal-to-noise ratio and the samples with faults are rare, which affects the generalization capability of the model, making it impossible to achieve accurate diagnosis. To solve this problem, the selection of time-frequency (TF) maps was considered in this paper through reinforcement learning. The TF maps are built by four classical TF characterization methods such as short-time Fourier transform and synchro squeezing transform. And the match-reinforcement learning time frequency selection (MRLTFS) fault diagnosis model is proposed to extract the fault-related features. Experiments show that the proposed MRLTFS method is superior to existing methods in robustness, generalization and feature selection capability.