스마트미디어저널 (Smart Media Journal)

  • 제6권2호
  • /
  • Pages.33-41
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  • 2017
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  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
권호 표지

데이터 큐브 모델과 SVM을 이용한 철도 선로전환기의 교체시기 탐지

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

  • 최용주 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 오지영 ((주)세화 부설연구소 연구개발팀) ;
  • 박대희 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 정용화 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 김희영 (고려대학교 공공정책대학 국가통계전공)
  • 투고 : 2017.06.07
  • 심사 : 2017.06.29
  • 발행 : 2017.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

철도 선로전환기는 열차의 진로를 현재의 궤도에서 다른 궤도로 제어하는 장치이다. 선로전환기의 노후화로 인한 이상 상황은 탈선 등과 같은 심각한 문제를 발생할 수 있기 때문에, 선로전환기의 적절한 교체시기를 결정하는 것은 매우 중요하다. 본 논문에서는 국내 철도 현장에서 획득한 선로전환기의 전류신호로부터 다차원 데이터 큐브를 구성하고 OLAP(On-Line Analytical Processing) 분석을 통하여 체계적으로 "교체가 필요한 데이터"와 "교체 시점이 아닌 데이터" 집합을 정제하여 분류하였다. 또한 선로전환기의 교체시기 탐지 문제를 이진 분류 문제로 해석하여 이진 분류기의 대표적 모델인 SVM(Support Vector Machine)을 탐지기로 설계함으로써 선로전환기의 노후화에 따른 적절한 교체시기를 탐지하는 시스템을 제안한다. 이때, 입력되는 전류 신호를 DWT(Discrete Wavelet Transform)와 PCA(Principal Components Analysis) 기법으로 고차원의 특징벡터 신호를 정보의 손실을 최소화하면서 저차원의 특징벡터로 변환한다. 실제 국내에서 운행 중인 선로전환기의 이상상황 정보가 포함된 대규모의 전류 신호를 이용하여 제안하는 시스템의 성능을 실험적으로 검증한 바 98%를 넘는 탐지 정확도를 확인하였다.

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%.

키워드

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