The current data-level and algorithm-level based imbalanced fault diagnosis methods have respective limitations such as uneven data generation quality and excessive reliance on minority class information. In response to these limitations, this study proposes a novel digital twin-assisted framework for imbalanced fault diagnosis. The framework begins by analyzing the nonlinear kinetic characteristics of the gearbox and establishing a dynamic simulation model assisted by digital twin technology to generate high-fidelity simulated fault data. Subsequently, a subdomain adaptive mechanism is employed to align the conditional distribution of the subdomains by minimizing the dissimilarity of fine-grained features between the simulated and real-world fault data. To improve the fault tolerance of the model's diagnosis, margin-aware regularization is designed by applying significant regularization penalties to the fault data margins. Experimental results from two gearboxes demonstrate that, compared to the recent data-level and algorithm-level based imbalanced fault diagnosis methods, the proposed framework holds distinct advantages under the influence of highly imbalanced data, offering a fresh perspective for addressing this challenging scenario. In addition, the effectiveness of subdomain adaptive mechanism and margin-aware regularization is verified through the ablation experiment.
|Journal||Reliability Engineering and System Safety|
|Early online date||23 Jul 2023|
|Publication status||E-pub ahead of print - 23 Jul 2023|
Bibliographical noteCopyright © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
This research is supported by the National Natural Science Foundation of China (No. 52275104 ) and the Natural Science Fund for Excellent Young Scholars of Hunan Province (No. 2021JJ20017 ).
© 2023 Elsevier Ltd
- Digital twin
- Imbalanced fault diagnosis
- Margin-aware regularization
- Subdomain adaptive mechanism