Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network
| العنوان: | Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network |
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| المؤلفون: | Yongchao Zhang, Ke Feng, J. C. Ji, Kun Yu, Zhaohui Ren, Zheng Liu |
| المصدر: | IEEE/ASME Transactions on Mechatronics. 28:1070-1080 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Industrial Engineering & Automation, Control and Systems Engineering, 0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering, 0913 Mechanical Engineering, Electrical and Electronic Engineering, Computer Science Applications |
| الوصف: | Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach. |
| وصف الملف: | application/pdf |
| تدمد: | 1941-014X 1083-4435 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63eee0fc656a0942916e6650ad464934 https://doi.org/10.1109/tmech.2022.3218771 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....63eee0fc656a0942916e6650ad464934 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1941014X 10834435 |
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