Partial Fault Diagnosis for Rolling Bearing Based on Interpretable Partial Domain Adaptation Network

In recent years, with the in-depth development of intelligent technology, the application of deep learning combined with domain adaptive methods in mechanical cross-domain fault diagnosis has increased. However, in the actual environment, the state categories of the target domain that need to be tested may only constitute a subset of the source domain. A partial fault diagnosis method for rolling bearings is proposed to aim at the partial domain adaptive problem in mechanical fault classification. The method introduces the temperature decay strategy into the traditional softmax function, sharpens the weight distribution difference between the shared and outlier classes, uses the obtained class-level weights to weigh the source samples, and combines with the confrontation strategy to achieve domain adaption. In addition, a correction function is added to the fault classifier to improve the classification accuracy of the source domain samples. Three different bearing datasets were used to verify the effect of the proposed method. Finally, the interpretability of the proposed model is analyzed and explained by explainable artificial intelligence (XAI) technology.