Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions
文献类型：期刊论文
| 作者 | Li, Yuan; Li, Jingwei; Wang, Huanjie ; Liu, Chengbao; Tan, Jie1
|
| 刊名 | RELIABILITY ENGINEERING & SYSTEM SAFETY
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 242页码:13 |
| ISSN号 | 0951-8320 |
| 关键词 | Remaining useful life Ensemble learning Attention mechanism Convolutional neural network Transfer learning |
| DOI | 10.1016/j.ress.2023.109748 |
| 通讯作者 | Tan, Jie(jie.tan@ia.ac.cn) |
| 英文摘要 | Remaining useful life (RUL) prediction is essential in enhancing the safety and reliability of rotating machinery. Deep learning techniques have been extensively researched and demonstrated promising results in RUL prediction tasks. But most existing models are designed for machinery equipment in a specific condition. In this case, a novel prediction method, knowledge-enhanced convolutional Transformer ensemble model (KE-CTEM), is proposed in this study. First, a feature extraction neural network (FENN) is introduced to extract features and transfer the working conditions information of existing datasets as knowledge to downstream RUL prediction tasks. Then, a convolutional Transformer model is leveraged to capture the input data degradation patterns and predict RUL values. Finally, knowledge-enhanced strategy and ensemble strategy are proposed to enhance the robustness of the model and improve the prediction accuracy.To verify the practicality and effectiveness of the proposed method, run-to-failure data of bearings from PRONOSTIA platform are utilized for RUL prognostics. Compared with several representative and stateof-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method. And ablation study indicates the high efficiency and robustness of each module within the proposed model. Compared with representative RUL prediction methods, the proposed KE-CTEM demonstrates superior performance in terms of RMSE and MAPE with a reduction of 32.0% and 16.2%, respectively. |
| WOS关键词 | NEURAL-NETWORK ; MODEL |
| 资助项目 | National Key Research and Development Program of China[2022YFB3304602] ; National Nature Science Foundation of China[62003344] |
| WOS研究方向 | Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001108579900001 |
| 资助机构 | National Key Research and Development Program of China ; National Nature Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55083]  |
| 专题 | 中科院工业视觉智能装备工程实验室 |
| 通讯作者 | Tan, Jie |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd Haidian Dist, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yuan,Li, Jingwei,Wang, Huanjie,et al. Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions[J]. RELIABILITY ENGINEERING & SYSTEM SAFETY,2024,242:13. |
| APA | Li, Yuan,Li, Jingwei,Wang, Huanjie,Liu, Chengbao,&Tan, Jie.(2024).Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions.RELIABILITY ENGINEERING & SYSTEM SAFETY,242,13. |
| MLA | Li, Yuan,et al."Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions".RELIABILITY ENGINEERING & SYSTEM SAFETY 242(2024):13. |
入库方式： OAI收割
来源：自动化研究所
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