NDLI: Data-driven model development using Support Vector Machine for railway Overhead Contact Wire maintenance

Content Provider	IEEE Xplore Digital Library
Author	Yuan, F.Q. Lu, J.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Eng. & Safety, Univ. of Tromso - The Arctic Univ. of Norway, Tromso, Norway (Yuan, F.Q.; Lu, J.M.)
Abstract	The fault on Overhead Contact Wire (OCW) causes traffic disruptions in railway transportation. The inspection and maintenance on OCW is inconvenient as it locates highly above the ground. Condition monitoring system has been installed to monitor the status of the OCW automatically. Huge amount of data have been collected by these systems. However, a general problem of these systems is the analysis on these data is weak. The condition monitoring system has been devalued due to this. This paper aims to develop data driven Support Vector Machine (SVM) model to extract useful information from the huge amount of data. This data driven model evaluate the OCW status without requiring understanding the physical mechanism of the fault. Maintenance points are suggested based on the output of the SVM model.
Starting Page	78
Ending Page	82
File Size	1858798
Page Count	5
File Format	PDF
e-ISBN	9781467380669
DOI	10.1109/IEEM.2015.7385612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wires Support Vector Machine (SVM) Artificial neural networks Data driven model Maintenance engineering maintenance optimization Data models Acceleration Kernel Overhead Contact Wire (OCW)
Content Type	Text
Resource Type	Article

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