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Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks

文献类型：期刊论文

作者Ren, Guanghao3; Wang, Yun3; Shi, Zhenyun2; Zhang, Guigang3; Jin, Feng1; Wang, Jian3
刊名APPLIED SCIENCES-BASEL
出版日期2023
卷号13期号:1页码:15
关键词remaining useful life estimation aero-engine convolutional autoencoder temporal convolutional network
DOI10.3390/app13010017
通讯作者Shi, Zhenyun(zyshi@buaa.edu.cn) ; Zhang, Guigang(guigang.zhang@ia.ac.cn)
英文摘要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.
WOS关键词PREDICTION
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
出版者MDPI
WOS记录号WOS:000909220900001
源URL[http://ir.ia.ac.cn/handle/173211/51126]  
专题数字内容技术与服务研究中心_智能技术与系统工程
通讯作者Shi, Zhenyun; Zhang, Guigang
作者单位1.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
2.Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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