| Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks | |
Ren, Guanghao1; Wang, Yun1 ; Shi, Zhenyun2; Zhang, Guigang1; Jin, Feng3; Wang, Jian1
| |
| Source Publication | APPLIED SCIENCES-BASEL
 |
| 2023 | |
| Volume | 13Issue:1Pages:15 |
| Corresponding Author | Shi, Zhenyun(zyshi@buaa.edu.cn) ; Zhang, Guigang(guigang.zhang@ia.ac.cn) |
| Abstract | With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency. |
| Keyword | remaining useful life estimation aero-engine convolutional autoencoder temporal convolutional network |
| DOI | 10.3390/app13010017 |
| WOS Keyword | PREDICTION |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Chemistry ; Engineering ; Materials Science ; Physics |
| WOS Subject | Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
| WOS ID | WOS:000909220900001 |
| Publisher | MDPI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51127 |
| Collection | 数字内容技术与服务研究中心_智能技术与系统工程 |
| Corresponding Author | Shi, Zhenyun; Zhang, Guigang |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China 3.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Ren, Guanghao,Wang, Yun,Shi, Zhenyun,et al. Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks[J]. APPLIED SCIENCES-BASEL,2023,13(1):15. |
| APA | Ren, Guanghao,Wang, Yun,Shi, Zhenyun,Zhang, Guigang,Jin, Feng,&Wang, Jian.(2023).Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks.APPLIED SCIENCES-BASEL,13(1),15. |
| MLA | Ren, Guanghao,et al."Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks".APPLIED SCIENCES-BASEL 13.1(2023):15. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment