Joint Wasserstein distance matching under conditional probability distribution for cross-domain fault diagnosis of rotating machinery

Transfer learning techniques have been extensively developed for the intelligent diagnosis of rotating machinery as a critical and valuable tool dedicated to minimizing the distributional discrepancies between different working conditions of the machine. However, conditional probability information about fault classes and the geometric features of data distribution is rarely considered in traditional distance metrics, invalidating cross-domain diagnostic models when faced with significant distributional discrepancies. To address these issues, a new cross-domain diagnostic algorithm is proposed via joint conditional Wasserstein distance matching. First, the conditional Bures–Wasserstein distance is constructed based on the second-order statistic cross-covariance operator, approximating the distributions in the source and target domains while constraining the geometry. Then, to avoid losing first-order fault data information, the conditional probability 1-Wasserstein distance is embedded to construct a joint distance adaptation. The entropy loss is introduced into the training process to build reliable pseudo labels for the target domain samples. In the proposed method, the samples and label features of different domains are mapped to the reproducing kernel Hilbert space (RKHS), and the Feature Extractor and Classifier modules of the model are jointly optimized to obtain a more robust diagnostic model. The proposed cross-domain diagnostic model is experimentally validated on bearing and gearbox datasets under variable loads and speeds with significant diagnostic performance compared to existing transfer models.