Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization

Shen Yan, Xiang Zhong, Haidong Shao, Yuhang Ming, Chao Liu and Bin Liu

Reliability Engineering and System Safety, 2023, vol. 239, issue C

Abstract: 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.

Keywords: Digital twin; Imbalanced fault diagnosis; Subdomain adaptive mechanism; Margin-aware regularization; Gearbox (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004362

DOI: 10.1016/j.ress.2023.109522

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