中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Fault diagnostics between different type of components: A transfer learning approach

文献类型:期刊论文

作者Li, Xudong2,3; Hu, Yang1; Li, Mingtao2,3; Zheng, Jianhua2,3
刊名Applied Soft Computing Journal
出版日期2019
ISSN号1568-4946
DOI10.1016/j.asoc.2019.105950
英文摘要Transfer learning methods have been successfully applied into many fields for solving the problem of performance degradation in evolving working conditions or environments. This paper expands the range of transfer learning application by designing an integrated approach for fault diagnostics with different kinds of components. We use two deep learning methods, Convolutional Neural Network (CNN) and Multi-layer Perceptron (MLP), to train several base models with a mount of source data. Then the base models are transferred to target data with different level of variations, including the variations of working load and component type. Case Western Reserve University bearing dataset and 2009 PHM Data Challenge gearbox dataset are used to validate the performance of proposed approach. Experimental results show that proposed approach can improve the diagnostic accuracy not only between the working conditions from the same component but also different components. © 2019 Elsevier B.V.
语种英语
源URL[http://ir.nssc.ac.cn/handle/122/7270]  
专题国家空间科学中心_空间技术部
作者单位1.Science and Technology on Complex Aviation System Simulation Laboratory, 9236 mailbox, Beijing, China
2.University of Chinese Academy of Science, Beijing, China;
3.National Space Science Center, Beijing, China;
推荐引用方式
GB/T 7714
Li, Xudong,Hu, Yang,Li, Mingtao,et al. Fault diagnostics between different type of components: A transfer learning approach[J]. Applied Soft Computing Journal,2019.
APA Li, Xudong,Hu, Yang,Li, Mingtao,&Zheng, Jianhua.(2019).Fault diagnostics between different type of components: A transfer learning approach.Applied Soft Computing Journal.
MLA Li, Xudong,et al."Fault diagnostics between different type of components: A transfer learning approach".Applied Soft Computing Journal (2019).

入库方式: OAI收割

来源:国家空间科学中心

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