SAE-FSC

Rolling element bearings are critical components in rotating machinery, where failures can cause severe downtime and safety risks. Existing fault diagnosis methods are predominantly supervised, requiring large amounts of labeled data across multiple operating conditions. However, in realistic industrial scenarios, such labeled datasets are scarce, and models trained on one regime often fail to generalize to others. To overcome this cross-domain generalization challenge, we propose a Siamese Attention Encoder–based few-shot cross-domain fault diagnosis (SAE-FSC) framework. The key novelty of this work lies in an attentionaugmented Siamese encoder that extracts highly discriminative and transferable time-series features, coupled with a composite objective function that jointly optimizes supervised cross-entropy, pairwise binary cross-entropy, and domain adversarial loss. This combination enforces intra-class domain invariant feature learning across multiple operating conditions. Extensive experiments on the Case Western Reserve University (CWRU) dataset under leave-one-fault-out (LOFO) and leave-two-fault-out (LTFO) protocols demonstrate robust generalization across unseen fault types, load conditions, and fault severities, achieving a prediction accuracy of 87% for 5 shot learning.