Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916564488421376 |
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| author | Tao, Laifa Zhao, Zhengduo Wang, Xuesong Li, Bin Zhan, Wenchao Su, Xuanyuan Li, Shangyu Huang, Qixuan Liu, Haifei Lu, Chen Lian, Zhixuan |
| author_facet | Tao, Laifa Zhao, Zhengduo Wang, Xuesong Li, Bin Zhan, Wenchao Su, Xuanyuan Li, Shangyu Huang, Qixuan Liu, Haifei Lu, Chen Lian, Zhixuan |
| contents | Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial failures. Traditional data-driven deep learning methods face challenges in practical settings due to inconsistent training and testing data distributions and limited generalization for long-term predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing Tao, Laifa Zhao, Zhengduo Wang, Xuesong Li, Bin Zhan, Wenchao Su, Xuanyuan Li, Shangyu Huang, Qixuan Liu, Haifei Lu, Chen Lian, Zhixuan Systems and Control Machine Learning Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial failures. Traditional data-driven deep learning methods face challenges in practical settings due to inconsistent training and testing data distributions and limited generalization for long-term predictions. |
| title | Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2501.07191 |