Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions
Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to significant distribution shifts. To bridge the gap between static offline models and dynamic online data, a novel asymmetric adaptation-based fault diagnosis method is proposed in this paper. Specifically, in the offline stage, we employ domain generalization techniques to extract domain-invariant features from multiple stable conditions and construct robust normalized fault prototypes as reference anchors. Subsequently, during online inference, we design an online test-time adaptation method based on a periodic prototype re-projection mech...
| Authors | Hongshuo Zhao, Zeyi Liu, Xiao He |
|---|---|
| Primary category | eess.SY |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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