Bearing Fault Diagnosis based on Residual Network Attention Mechanism

Abstract

Aiming at the problem that the detection of bearing fault diagnosis is rarely applied in the research of image classification, a new method based on residual network and attention mechanism is proposed to identify bearing fault diagnosis. One-dimensional vibration signals are transformed into two-dimensional time-frequency images by continuous wavelet transform (CWT), which are input into the model for classification. In order to solve the problem that the traditional convolutional neural network model ignores the low diagnostic accuracy of channel attention and spatial attention due to the loss of important features, the attention mechanism CBAM module is added to make up for the loss of channel features and spatial features in the traditional model. At the same time, the residual network Resnet combined with the attention mechanism can better capture the global information of the time frequency graph, and make up for the defects of the residual network module. The experimental results show that the model has high diagnostic accuracy in rolling bearing fault diagnosis, which proves that the proposed method is effective and feasible.