Fault prediction of power supply vehicle based on multi-state time series prediction learning

doi:10.13229/j.cnki.jdxbgxb20181290

	Fault prediction of power supply vehicle based on multi-state time series prediction learning
	Li, Wei 1,2,3; Zhou, Bing-Xiang 1,2,3; Jiang, Dong-Nian 1,2,3; Sun, Xiao-Jing 4
刊名	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
	2020-07-01
卷号	50 期号:4 页码:1532-1544
关键词	Forecasting Long short-term memory Nearest neighbor search Time series Vehicles Complex equipment Experimental analysis Nearest neighbors Operation situation Real-time operation Simulation systems Time series prediction Vehicle operations
ISSN号	16715497
DOI	10.13229/j.cnki.jdxbgxb20181290
英文摘要	The existing fault prediction methods are difficult to apply to large and complex equipment. Aiming at this situation, a fault prediction method based on multi-state time series dynamic trend prediction learning is proposed for power supply vehicle. Firstly, this method establishes a time series prediction model of power supply vehicle operation status based on Long Short Term Memory (LSTM) network, and predicts the future operation situation by combining the history and real-time operation data of power supply vehicle. Then, on the basis of obtaining the prediction situation, the improved -Nearest Neighbor (kNN) algorithm is used to analyze the correlation between the state change trend and the fault, and to predict the possible faults in the future. Experimental analysis is carried out on the simulation system of power supply vehicle. The results verify the validity and applicability of the proposed method. © 2020, Jilin University Press. All right reserved.
语种	中文
出版者	Editorial Board of Jilin University
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/115445]
专题	电气工程与信息工程学院
作者单位	1.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou; 730050, China; 2.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 4.Lanzhou Power Supply Vehicle Research Institute Co. Ltd., Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Li, Wei,Zhou, Bing-Xiang,Jiang, Dong-Nian,et al. Fault prediction of power supply vehicle based on multi-state time series prediction learning[J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),2020,50(4):1532-1544.
APA	Li, Wei,Zhou, Bing-Xiang,Jiang, Dong-Nian,&Sun, Xiao-Jing.(2020).Fault prediction of power supply vehicle based on multi-state time series prediction learning.Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),50(4),1532-1544.
MLA	Li, Wei,et al."Fault prediction of power supply vehicle based on multi-state time series prediction learning".Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 50.4(2020):1532-1544.