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A Cross-domain Bearing Fault Diagnosis Method towards Unbalanced Data based on Universal Domain Adaptation

Bearing fault diagnosis is significantly important in industrial production, which can effectively avoid the economic loss and operational accident caused by the failure of rotating machinery. However, there are still many difficulties in bearing fault diagnosis. For one thing, sufficient data are difficult to be collected for each bearing fault, so the training results are not reliable. For another thing, it is difficult for train set and test set to meet consistent distributions due to continuously changing operating conditions, leading to the difficulty of applying the trained model to the actual bearing data. In this paper, a cross-domain bearing fault diagnosis method toward unbalanced data based on universal domain adaptation (UniDA) is proposed. Firstly, wasserstein generative adversarial network with gradient penalty (WGAN-GP) is adapted to generate data to obtain balanced bearing data sets. Then, UniDA is employed to achieve cross-domain fault diagnosis in bearing diagnosis. Finally, the comparison results show that the proposed method is effective in cross-domain bearing fault diagnosis toward unbalanced data.