한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
권호 표지
DOI QR코드

DOI QR Code

데이터 기반 철도 위험도평가 기준에 관한 연구

Research on Data-Driven Railway Risk Assessment Criteria

  • 박은경 (동양대학교 철도전기융합학과)
  • Eun-Kyung Park (Dept. of Electric Railway Convergence Science, DongYang University)
  • 투고 : 2023.06.02
  • 심사 : 2023.08.17
  • 발행 : 2023.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

2014년 철도운영자들과 철도시설관리자의 자발적인 안전관리를 정착시키기 위하여 철도안전법에서 '철도안전관리체계'를 강화하였다. 이에 따라 위험도를 평가하고 위험도를 관리하기 위한 안전대책을 수립하고 시행할 것으로 판단하였으나 현재 위험도 평가 체계는 개별 분야 내 단편적인 안전관리 수준으로 진행되고 있다. 또한 안전관리체계의 기술기준에서 철도 운영기관의 위험도 평가에 관한 내용이 의무사항으로 명시되어 있어 철도시설 및 철도차량유지보수의 위험도 평가를 위한 표준화된 기준이 필요하다. 따라서 본 논문에서는 최근 10년간 철도사고 데이터를 분석하여 먼저 철도 위험도 수준을 검증하였고, 철도차량유지보수 부분에서 데이터를 기반으로 개발된 상태기반 스마트 유지보수 시스템 사례를 통해 위험도를 효과적으로 평가하고 관리할 수 있는 표준화된 프레임워크를 제시하였다.

The Railway Safety Act of 2014 strengthened the 'Railway Safety Management System' to establish autonomous safety management for railway operators and railway facility managers. Accordingly, it is required to establish and implement risk assessment and safety measures for risk management. However, the current risk assessment system is carried out at the fragmented safety management level within individual fields, which has caused difficulties in establishing and implementing risk assessment and safety measures. In addition, the technical standards of the safety management system stipulate that risk assessment of railway operators is mandatory, so standardized standards for risk assessment of railway facilities and railway vehicle maintenance are needed. Therefore, in this paper, we first verified railway risks by analyzing railway accident data for the last 10 years, and proposed a standardized framework to effectively assess and manage risks through a case study of a condition-based smart maintenance system developed based on railway vehicle maintenance data.

키워드

과제정보

이 논문은 2022년도 동양대학교 학술연구비의 지원으로 수행되었음.

참고문헌

  1. J. Jeong and J. Seo, "A Study of Establishment and Operation Management of a Data-Based Risk Analysis System - Focusing on the Case of Korail," J. of the Korean Society for Urban Railway, vol. 10, no. 1, 2022, pp. 1219-1230. https://doi.org/10.24284/JKOSUR.2022.03.10.1.1219
  2. S. Kim and Y. Cho, "Development of Risk-based Railway Safety Management System Architecture)," The Korean Society for Railway, Jeju, Korea, Oct. 2011.
  3. I. Lee, "A Study on the Improvement Plan for Railway Risk Assessment in Operational Stage Using Railway Safety Information Analysis," Doctoral Dissertation, Dongyang University Graduate Department of Electrical & Communication Engineering, 2022.
  4. KAIA, "Condition-based Smart Maintenance Core Technology Development Final Report," R&D Report, 2018.
  5. C. Park, S. Kwak, J. Park, and J. Wang, "A Study on the Development of Railway Risk Assessment Information Management System," The Korean Society for Railway, Busan, Korea, Nov. 2006.
  6. B. Song, M. Kim, D. Moon, D. Lee, and H. Lee, "Development of Railway Accidents Causal Analysis System," J. of the Korean Society for Railway, vol. 13, no. 4, 2010, pp. 455-461.
  7. H. Nam, H. Lee, J. Kim, and D. Kim, "A study on the implementation of the domestic railroad RCM based on the analysis of railway accidents," J. of the Korean Data & Information Science Society, vol. 30, no. 2, 2019, pp. 415-427. https://doi.org/10.7465/jkdi.2019.30.2.415
  8. H. Kim, "A Study on The Real-Time Data Collection, Analysis, Processing Intelligent IoT" J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 2, 2019, pp. 317-322.
  9. KRRI, "Development of assessment techniques for risk-based safety management system," R&D Report, 2017.
  10. D. Shin, C. Park, E. Chae, and J. Lee, "Case Study on Risk Assessment of Railway Infrastructure Sections Using Acceptable Risk Matrix," J. of the Korean Society for Railway, vol. 20, no. 4, 2017, pp.550-557. https://doi.org/10.7782/JKSR.2017.20.4.550
  11. J. Jo, "Effectiveness of Normalization Pre-Processing of Big Data to the Machine Learning Performance" J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 3, 2019, pp. 547-552.
  12. K. Park, K. Kim, K. Ban, and Y. Kim, "Design and Implementation of Cloud-based Sensor Data Management System," J. of the Korea Institute of Electronic Communication Sciences, vol. 5, no. 6, 2010, pp. 672-677.