Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks
	Zheng ZY(郑泽宇)1,2,3,4 ; Yang TJ(杨天吉)1,2,3,4; Yao QF(么庆丰)1,2,3,4; Liu Z(刘智)1,2,3,4
	2019
会议日期	June 17-20, 2019
会议地点	San Francisco, CA, United states
关键词	Neural Networks Ensemble Learning Empirical Mode Decomposition Remaining Useful Life
页码	1-6
英文摘要	Bearing remaining useful life (RUL) prediction plays a key role in guaranteeing safe operation and reducing maintenance costs. In this paper, we present a novel deep learning method for RUL estimation approach through time Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). EMD can reveal the nonstationary property of bearing degradation signals effectively. After acquiring time-series degradation signals, namely Intrinsic Mode Functions (IMF), we can utilize the featured information as the input of Convolution layer of models. Here, we introduce an EMD-CNN model structure, which keeps the global and local information synchronously compared to a traditional CNN. In order to get a more accurate prediction, an ensemble model with several weighting methods are proposed, where the experiment indicates an improvement of performance.
产权排序	1
会议录	2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5386-8357-6
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/25663]
专题	沈阳自动化研究所_数字工厂研究室
通讯作者	Yao QF(么庆丰)
作者单位	1.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 2.University of Chinese Academy of Sciences, Beijing, 100049, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
推荐引用方式 GB/T 7714	Zheng ZY,Yang TJ,Yao QF,et al. Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks[C]. 见:. San Francisco, CA, United states. June 17-20, 2019.