An Improved Fault Diagnosis Method for Rolling Bearings Based on 1D_CNN Considering Noise and Working Condition Interference

Abstract

Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable operation of rolling bearings. However, interference caused by environmental noise and variable working conditions can affect the data. To solve this problem, we propose an improved fault diagnosis method called deep convolutional neural network based on multi-scale features and mutual information (MMDCNN). In our approach, a multi-scale convolutional layer is placed at the front end of a 1D_CNN to maximize the retention of the multi-scale initial features. Meanwhile, the key fault features are further enhanced adaptively by introducing a self-attention mechanism. Then, the composite loss function is constructed by maximizing mutual information as an auxiliary loss based on cross-entropy loss; thus, the proposed method can extract robust fault features with high generalization performance. To demonstrate the superiority of MMDCNN, we compared the performance of our scheme with several existing deep learning models on two datasets. The results show that the proposed model successfully achieves bearing fault diagnosis with interference from noise and variable working conditions, possessing a powerful fault feature extraction capability.