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-Nian1,2,3 ; Sun, Xiao-Jing 4
	2020-07-01
发表期刊	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
ISSN	16715497
卷号	50 期号:4 页码:1532-1544
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.
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
DOI	10.13229/j.cnki.jdxbgxb20181290
收录类别	EI
语种	中文
出版者	Editorial Board of Jilin University
EI入藏号	20203509100890
EI主题词	Predictive analytics
EI分类号	921.5 Optimization Techniques - 922.2 Mathematical Statistics
来源库	Compendex
分类代码	921.5 Optimization Techniques - 922.2 Mathematical Statistics
引用统计	无
文献类型	期刊论文
条目标识符	https://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.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 3.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou; 730050, China; 4.Lanzhou Power Supply Vehicle Research Institute Co. Ltd., Lanzhou; 730050, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
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.