A Cross-Domain Bearing Fault Diagnosis Method with Multi-Source Incomplete Data

The cross-domain fault diagnosis method based on domain adaptation is a hot topic in recent years. It is difficult to collect a complete data set containing all fault categories in practice under the same working condition, leading to fault categories knowledge loss in the single source domain. To resolve the problem, a cross-domain fault diagnosis method with multi-source incomplete data is proposed in this study. First, the cycle generative adversarial network is used to learn the mapping between multi-source domains to complement the missing category data. Then, considering the domain mismatch problem, a multi-source domain adaption model based on anchor adapters is developed to obtain general domain invariant diagnosis knowledge. Finally, the fault diagnosis model is established by an ensemble of multi-classifier results. Extensive experiments on bearing data sets demonstrate that the proposed method in cross-domain fault diagnosis with multi-source incomplete data is effective and has a good diagnosis performance.