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Data Driven Detection of Railway Point Machines Failures

Abstract : In this paper, a novel approach to early detection of railway point machines failures is presented. Easily accessible data from Centralized Traffic Control (CTC) systems, along with meteorological data, are utilized to build a classification system recognizing risk factors for railway point machine failure. We present and discuss a framework that aims at extracting information from the raw railway logs, and discuss the issues that need to be solved to make the framework properly operational. We show that ensemble methods utilizing decision trees are able to provide meaningful classification accuracy for this problem.
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Contributor : Christophe Marsala <>
Submitted on : Thursday, December 12, 2019 - 3:31:41 PM
Last modification on : Wednesday, December 18, 2019 - 1:42:29 AM
Document(s) archivé(s) le : Friday, March 13, 2020 - 10:07:34 PM


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  • HAL Id : hal-02407540, version 1


Iwo Doboszewski, Simon Fossier, Christophe Marsala. Data Driven Detection of Railway Point Machines Failures. IEEE Symposium Series on Computational Intelligence (SSCI) - Computational Intelligence in Vehicles and Transportation Systems (CIVTS), Dec 2019, Xiamen, China. pp.1233-1240. ⟨hal-02407540⟩



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