System Development & Application
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2712-2717

Fault prediction for communication and signal equipment based on KNE-BPNN

Li Chenguang1
Qiao Shuai1
Yang Xiaojie1
Xie Weifan2
Li Chuanzi1
Li Junhong1
1. College of Mathematics & Information Science, Hebei Normal University, Shijiazhuang 050024, China
2. School of Information Science & Engineering, Southeast University, Nanjing 211189, China
DOI: 10.19734/j.issn.1001-3695.2018.03.0201

Abstract

Based on the practical problems, such as frequent failures, low operating efficiency and lacking of effective fault prediction methods for railway communication and signal(C&S) equipment, this paper proposed a fault prediction model for C&S equipment based on K-means—neighborhood approximate conditional entropy and BP neural network(KNE-BPNN). Firstly, it used a sample reduction algorithm based on K-means clustering to reduce the redundant samples in the equipment failure decision table. Secondly, it used neighborhood approximate conditional entropy attribute reduction theory to reduce the non-essential attributes in the fault decision table after sample reduction. Finally, it trained the BP neural network by using the reduced sample set, and trained the model until its output met the expected requirement. The experimental results show that the prediction precision and generalization performance of the KNE-BPNN fault prediction model can meet the actual requirements.

Foundation Support

国家自然科学基金资助项目(61573127,61502144)
河北省科技厅重点研发计划资助项目(16455702D)
河北师范大学基金资助项目(L2017B09,S2016Y13)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0201
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: System Development & Application
Pages: 2712-2717
Serial Number: 1001-3695(2019)09-033-2712-06

Publish History

[2019-09-05] Printed Article

Cite This Article

李晨光, 乔帅, 杨晓杰, 等. 基于KNE-BPNN的电务设备故障预测 [J]. 计算机应用研究, 2019, 36 (9): 2712-2717. (Li Chenguang, Qiao Shuai, Yang Xiaojie, et al. Fault prediction for communication and signal equipment based on KNE-BPNN [J]. Application Research of Computers, 2019, 36 (9): 2712-2717. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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