A domain adaptation method for bearing fault diagnosis using multiple incomplete source data

The 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 domain adaptation method for bearing fault diagnosis using multiple incomplete source 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 fault diagnosis with multiple incomplete source data is effective and has a good diagnosis performance.