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


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: Copyright © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license Funding Information: 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 ). Publisher Copyright: © 2023 Elsevier Ltd
Uncontrolled Keywords: Digital twin,Gearbox,Imbalanced fault diagnosis,Margin-aware regularization,Subdomain adaptive mechanism,Safety, Risk, Reliability and Quality,Industrial and Manufacturing Engineering
Publication ISSN: 0951-8320
Last Modified: 11 Jul 2024 07:25
Date Deposited: 26 Jul 2023 14:17
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Related URLs: https://www.sci ... 4362?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-11
Published Online Date: 2023-07-23
Accepted Date: 2023-07-22
Authors: Yan, Shen
Zhong, Xiang
Shao, Haidong
Ming, Yuhang
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Liu, Bin



Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 23 July 2024.

License: Creative Commons Attribution Non-commercial No Derivatives

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