A Domain Adaptation Method for Bearing Fault Diagnosis Based on Pseudo Label and Wavelet Neural Network

Aiming at the problem of insufficient data and label shortage in equipment health status identification, this article proposes a domain adaptation transfer learning model combined with Morlet wavelet kernel convolution. The model enhances the fault feature extraction capability through Morlet wavelet kernel convolution and tunable-Q wavelet transform (TQWT) sparse representation. To process the unlabeled target domain data, a pseudo-label technique and a label dynamic update mechanism are proposed. First, pseudo labels are assigned to the unlabeled data based on feature metrics evaluation, and accordingly, the target domain data are divided into two parts with and without pseudo labels. Subsequently, a step-by-step training strategy is adopted. First, the pseudo-labeled data are used to train the model with the source domain data to extract similar fault features, and then, the feature alignment training is carried out through the domain adversarial framework using the unlabeled data. The label dynamic update mechanism dynamically adjusts the pseudo-label assignments during the training process to ensure that high-confidence data are involved in the training. Experiments show that this method effectively improves the fault feature extraction effect and diagnosis performance, which is more advantageous than other transfer methods.