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Article

Reliability Analysis of Screen Doors and Door’s Maintenance Strategies for Demand Responsive Self-Driving Shuttles

스크린도어 신뢰도 분석과 이를 활용한 수요응답형 자율주행 셔틀의 출입문 유지보수 전략

Alain Anthony MORRIS1
,
Tae-jin KIM2
,
Hyun KIM3*
모리스 알레인 안토니1
,
김 태진2
,
김 현3*
1Master Student, Department of IT Energy Convergence, Korea National University of Transportation, Chungju 27469, Korea
2Professor, Department of Public Administration, Korea National University of Transportation, Chungju 27469, Korea
3Professor, Department of IT Energy Convergence (Major in AI Mobility), Korea National University of Transportation, Chungju 27469, Korea
1국립한국교통대학교 교통에너지융합학과 석사과정
2국립한국교통대학교 행정학과 교수
3국립한국교통대학교 교통에너지융합학과 교수
*Corresponding Author
31 December 2025. pp. 830-846
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Abstract

This study analyzes the reliability and maintenance performance of platform screen doors (PSDs) in the Seoul Metropolitan Area Railway using 10,078 failure records from 279 stations on 18 lines (2019-2024). Weibull lifetime modeling indicates clear wear-out behavior across components (shape parameter β ≈ 1.63-2.30), with electrical stops and motors identified as the most failure-prone elements. Significant manufacturer heterogeneity was observed, with mean time between failures ranging from ≈ 350 to 560 days, underscoring procurement and design effects on lifecycle reliability. A Monte Carlo life-cycle simulation compared four maintenance strategies-reactive, preventive, condition-based, and predictive-under stochastic cost and failure conditions. Condition-based maintenance achieved the lowest 10-year cost (USD 7.9 million) while maintaining availability above 99.6%, whereas predictive maintenance minimized failures and safety incidents at moderately higher cost (USD 9.0 million). Preventive maintenance proved the least efficient under wear-out regimes. The results support a hybrid policy combining predictive thresholds for high-risk modules (motors, electrical stops) with condition-based cycles for lower-risk components. Beyond PSD applications, the validated Weibull-Monte Carlo framework can be reformulated for autonomous and demand- responsive transit (DRT) systems by expressing exposure in operational cycles and adapting cost penalties for missed pickups and availability losses. This transferable reliability-engineering approach provides a quantitative foundation for predictive maintenance and availability optimization in both fixed and mobile, fully driverless transit environments.

Keywords
autonomous, availability
demand-responsive transport (DRT)
monte carlo simulation
platform screen door (PSD)
reliability

본 연구는 2019년부터 2024년까지 18개 노선, 279개 역 10,078건의 고장 기록을 이용하여 수도권 전철 승강장 스크린도어(PSD)의 신뢰성 및 유지보수 성능을 분석한다. 와이블 수명 모델링은 구성품 전반에 걸쳐 명확한 마모거동(형상 모수 β ≈ 1.63-2.30)을 나타내며, 전기 정지장치와 전동기가 가장 고장 발생 가능성이 높은 요소로 확인되었다. 제조업의 신뢰성은 상당한 이질성이 관찰되었으며, 평균고장간격(MTBF)은 약 350일에서 560일 사이로, 설계가 수명주기 신뢰성에 영향을 미치고 있었다. 몬테카를로 수명주기 시뮬레이션은 확률적 비용 및 고장 조건에서 사후 대응, 예방, 조건 기반, 예측의 4 가지 유지보수 전략을 비교했다. 상태 기반 유지보수는 가용성을 99.6% 이상으로 유지하면서 10년 동안 가장 낮은 비용(790만 달러)을 달성한 반면, 예측유지보수는 다소 높은 비용(900만 달러)으로 고장 및 안전사고가 최소화되었다. 예방 유지보수는 마모 체계에서 가장 비효율적인 것으로 나타났다. 이 결과는 고위험 모듈(모터, 전기 정지 장치)에 대한 예측 임계값과 저위험 구성 요소에 대한 상태 기반 주기를 결합한 하이브리드 정책의 근거가 된다. PSD 적용 외에도, 검증된 Weibull-Monte Carlo 프레임워크는 운영 주기에서의 노출을 표현하고, 픽업 미실시 및 가용성 손실에 대한 비용 페널티를 조정함으로써 자율 주행 및 수요대응 대중교통(DRT) 시스템에 맞게 재구성될 수 있다. 이러한 이전 가능한 신뢰성 공학적 접근 방식은 완전 무인 자율주행 대중교통 환경에서 예측 유지보수 및 가용성 최적화를 위한 정량적 기반을 제공하고 있다.

키워드
자율주행
가용성
수요응답형 대중교통(DRT)
몬테카를로 시뮬레이션
플랫폼스크린도어(PSD)
신뢰성
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 43
  • No :6
  • Pages :830-846
  • Received Date : 2025-10-20
  • Revised Date : 2025-10-27
  • Accepted Date : 2025-11-17
  • DOI :https://doi.org/10.7470/jkst.2025.43.6.830
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