Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment

Abstract

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of engineering equipment. Many intelligent diagnosis methods have been successfully developed. However, the performances of traditional fault diagnosis methods are affected by noisy labels and environment which widely exist in realistic industrial applications. This article proposed a novel FD method of RB based on wavelet transform (WT) and an improved residual neural network (IResNet), named WT-IResNet. The proposed WT-IResNet approach uses a new pooling layer for dimension reduction and a global singular value decomposition (SVD) adaptive strategy for feature extraction. Furthermore, the original softmax layer and the logistic loss for training are replaced by a new loss function containing two adjustable parameters to address fault diagnosis with label noises. Two typical bearing failure datasets are used to evaluate the feasibility and effectiveness of WT-IResNet under noisy labels and noisy environment respectively. The experimental results indicate that WT-IResNet has better robustness against noise in comparison with other methods. Whatever under noisy labels or noisy environment, the performance of WT-IResNet outperforms other methods.