Bearing Fault Diagnosis Based on Local Manifold Discriminant Domain Adaptation

Transfer learning (TL) has been widely applied in bearing defect diagnosis across various working conditions. However, some existing methods overlook the local and discriminative manifold structures hidden in samples with different distributions, which may degrade model diagnostic performance. In this study, a local manifold discriminant domain adaptation (LMDDA) TL method is proposed for bearing fault diagnosis. The source and target domains are first converted in manifold feature subspace to obtain robust feature representations. Then, an intermediate domain with a similar distribution to the target domain is generated from the source domain in the potential common subspace to reduce the distribution difference between source and target domains. Meanwhile, the local generated discrepancy metric is used to evaluate the discriminability of data, preserve the local manifold structure of the generated data, and align the local data. The intermediate and target domains are aligned by the maximum mean discrepancy metric to enhance the consistency of the local and global structures of data. On this basis, the low-rank constraint is introduced to guarantee the correlation between source and target data in virtue of the block diagonal property, which can improve the generalization ability and performance stability of the model. Finally, the least-squares method is utilized to achieve bearing fault diagnosis. A series of cross-domain fault diagnosis experiments was carried out to verify the effectiveness and superiority of the proposed method, and the results illustrate that LMDDA can accurately detect diverse fault types and handle intricate cross-domain adaptive scenarios.