Rolling Bearing Fault Diagnosis Based on MResNet-LSTM

Abstract

In response to the challenges of weak fault characteristics and complex and variable working conditions of roll-ing bearings in strong noise environments, a diagnostic methodology is proposed. This method integrates a multi-scale residual neural network (MResNet) and a long short-term memory (LSTM) neural network to achieve fault diagnosis of bearings under both constant and variable rotational speed working conditions. First, complete an ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that is applied for vibration signal denoising. Secondly, a dropout layer is introduced between multi-scale residual blocks to prevent net-work overfitting, and the powerful time series information capture ability of LSTM is combined to improve di-agnostic accuracy. Finally, experimental verification is conducted using constant and variable speed bearing data sets. The results show that the proposed approach maintains strong diagnostic capabilities when the rotat-ing speed conditions change, and the signal is heavily polluted by noise.