Modified Bi-Directional LSTM Neural Networks for Rolling Bearing Fault Diagnosis

Abstract

The rolling bearing fault diagnosis with vibration data is critical to the reliability and the safety of rotating machinery. According to the non-stationary characteristics and the simple logical structure characteristics of rolling bearing vibration data, a rolling bearing fault diagnosis method based on modified bidirectional long short-term memory (Bi-LSTM) neural network is put forward in this paper. Firstly, original vibration data are decomposed into time-frequency feature with the combination of Daubechies 10 wavelet packet transform and Symlets 8 wavelet packet transform. Then, we design bidirectional long-term memory (Bi-LTM) neural network, the Bi-LTM neural network only uses long-term memory to process rolling bearing feature data and get the result of fault diagnosis. In order to enhance functionality of the Bi-LTM internal activation function, the Bi-LTM internal function uses softsign. We evaluate our models on a standard dataset. Moreover, given the analytical results, compared to Bi-LSTM, the proposed Bi-LTM method further reduces the rolling bearing fault diagnosis error rate by 6 times. Numerical and simulation results verify that the rolling bearing fault diagnosis method based on the proposed method is justified.