Fault diagnostics between different type of components: A transfer learning approach

doi:10.1016/j.asoc.2019.105950

	Fault diagnostics between different type of components: A transfer learning approach
	Li, Xudong 2,3; Hu, Yang 1; Li, Mingtao 2,3; Zheng, Jianhua 2,3
刊名	Applied Soft Computing Journal
	2019
ISSN号	1568-4946
DOI	10.1016/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
内容类型	期刊论文
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).