Adaptive Topology-Aware Siamese Network for Cross-Domain Fault Diagnosis With Small Samples

The emerging graph convolutional network (GCN) provides a way to extract additional relationship information and has demonstrated superior performance in fault diagnosis. However, the GCN-based methods have some limitations when applied to cross-domain fault diagnostics. First, the graph topology constructed according to certain criteria remains fixed while it may contain domain-specific information, which could hamper the extraction of domain-invariant features. Second, the non-Euclidean structure of the graph hinders its augmentation through mixing and interpolation, bringing its application challenges in scenarios with small samples. To address these limitations, this article proposes an adaptive topology-aware siamese network (ATA-SN) for cross-domain fault diagnosis with small samples. Specifically, the multichannel data are constructed as a complete graph as input. The adaptive topology-aware module updates and augments the graph simultaneously through biased uncertainty edge perturbation in a nonparametric manner. In combination with a contrastive loss, augmented data with clear class boundaries can be obtained. A statistical metrics-based style loss is used for domain-invariant feature learning. A pair of updated graphs is then fed into the siamese framework, which utilizes the semantic consistency of the paired graphs to recalibrate the contribution of the augmented data to the gradient descent, thus ensuring the effectiveness of the graph augmentation and domain adaptation. Comparative studies and cross-domain fault diagnosis experiments indicate the superiority and effectiveness of the proposed method even in scenarios with extremely limited training data. The code of ATA-SN is released at https://github.com/CQU-ZixuChen/ATA-SN.