A WOA-SVMD and multi-scale CNN-transformer method for fault diagnosis of motor bearing

Long-term operation of electric motor under complex and extreme conditions can lead to unpredictable failures, therefore, accurate diagnosis of electric motor failures has been valued by scholars and engineers. However, the noise in the vibration signals of the motor’s rolling bearings has a profound impact on the diagnostic performance of a model in the process of feature extraction and fault classification. Aiming at vibration signal denoising and accurate fault classification, in this study, a novel method based on WOA-SVMD and multi-scale CNN-Transformer is proposed. Firstly, the Whale Optimization Algorithm (WOA) is employed to obtain the optimal parameters of Successive Variational Mode Decomposition (SVMD), which is then used to decompose the signal into Intrinsic Mode Functions (IMFs). Secondly, uncorrelated components are removed based on the correlation coefficient method, the left IMFs are reconstructed into new signals. Thirdly, local and global features of the signal are adequately extracted using multi-scale Convolutional Neural Network (CNN) and transformer. Finally, fault type is classified using the softmax function. The experimental results show that the proposed method can effectively reduce the noise interference, and the accuracy of fault diagnosis reaches 99.24% on the CWRU dataset and 99.68% on the PU dataset.