# Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions

arXiv: https://arxiv.org/abs/2605.24457 · HTML: https://mlsanity.com/paper/2605.24457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 23 May 2026.

## Builds on (most cited first, 11 of 11)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Parameter-free Online Test-time Adaptation](https://mlsanity.com/paper/2201.05718/) (2022)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve](https://mlsanity.com/paper/2102.03924/) (2021)
- [In Search of Lost Online Test-time Adaptation: A Survey](https://mlsanity.com/paper/2310.20199/) (2023)
- [Domain Adaptive Transfer Learning for Fault Diagnosis](https://mlsanity.com/paper/1905.06004/) (2019)
- [Multi-mode Fault Diagnosis Datasets of Gearbox Under Variable Working Conditions](https://mlsanity.com/paper/2403.12521/) (2024)
- [Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects](https://mlsanity.com/paper/2412.19497/) (2024)
- [Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions](https://mlsanity.com/paper/2406.06607/) (2024)
- [Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis](https://mlsanity.com/paper/2506.17740/) (2025)

## Abstract (arXiv)

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 mechanism to dynamically update prototype positions. Furthermore, we utilize the geometric distribution derived from anchors to guide the updates of classifiers and adopt an asymmetric learning rate strategy for the feature extractor and classifier. The proposed approach ensures rapid adaptation to new transitional conditions while preserving the discriminative power inherited from the offline domain generalization initialization. Experimental results demonstrate that this mechanism effectively leverages offline generalized knowledge to guide online inference, significantly improving robustness in non-stationary environments.