Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation
文献类型：期刊论文
| 作者 | Xu CZ(须成忠)2; Ye KJ(叶可江)4; Yao QF(么庆丰)1,3; Yang, Wensi1,4 |
| 刊名 | INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
 |
| 出版日期 | 2019 |
| 页码 | 1-19 |
| ISSN号 | 0885-7458 |
| 关键词 | Convolutional neural networks Empirical mode decomposition Remaining useful life Reliability |
| 产权排序 | 2 |
| 英文摘要 | Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD-TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD-TCN model is improved by 10-20% than traditional convolutional algorithms. |
| 语种 | 英语 |
| WOS记录号 | WOS:000496196700001 |
| 资助机构 | National Key R&D Program of China [2018YFB1004804] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61702492] ; Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence ; Shenzhen Basic Research Program [JCYJ20170818153016513] |
| 源URL | [http://ir.sia.cn/handle/173321/25890]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.Department of Computer and Information Science, University of Macau, Macau, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.Shengzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shengzhen, China |
| 推荐引用方式 GB/T 7714 | Xu CZ,Ye KJ,Yao QF,et al. Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation[J]. INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING,2019:1-19. |
| APA | Xu CZ,Ye KJ,Yao QF,&Yang, Wensi.(2019).Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation.INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING,1-19. |
| MLA | Xu CZ,et al."Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation".INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING (2019):1-19. |
入库方式： OAI收割
来源：沈阳自动化研究所
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