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Fault diagnostics between different type of components: A transfer learning approach
Li, Xudong1,2; Hu, Yang3; Li, Mingtao1,2; Zheng, Jianhua1,2
Department空间技术部
Source PublicationApplied Soft Computing Journal
2019
DOI10.1016/j.asoc.2019.105950
ISSN1568-4946
Language英语
AbstractTransfer 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.
Indexed ByEI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7270
Collection空间技术部
Affiliation1.National Space Science Center, Beijing, China;
2.University of Chinese Academy of Science, Beijing, China;
3.Science and Technology on Complex Aviation System Simulation Laboratory, 9236 mailbox, Beijing, China
Recommended Citation
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).
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