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Replacement Condition Detection of Railway Point Machines Using Data Cube and SVM  

Choi, Yongju (고려대학교 컴퓨터융합소프트웨어학과)
Oh, Jeeyoung ((주)세화 부설연구소 연구개발팀)
Park, Daihee (고려대학교 컴퓨터융합소프트웨어학과)
Chung, Yongwha (고려대학교 컴퓨터융합소프트웨어학과)
Kim, Hee-Young (고려대학교 공공정책대학 국가통계전공)
Publication Information
Smart Media Journal / v.6, no.2, 2017 , pp. 33-41 More about this Journal
Abstract
Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.
Keywords
Railway Point Machines; Replacement Condition Detection; Data Cube; OLAP; SVM;
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Times Cited By KSCI : 5  (Citation Analysis)
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