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학술지
고속 열차 고장 발생 예측을 위한 연관 규칙 마이닝의 적용
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- 저자
- 김철홍, 김영덕, 염병수, 박정희
- 발행일
- 201603
- 출처
- 대한설비관리학회지, v.21 no.1, pp.59-65
- ISSN
- 1598-2475
- 출판사
- 대한설비관리학회
- 협약과제
- 15GC1500, 빅데이터 통합 플랫폼 기반 철도사고 위험예측 기술 개발(본과제명 : 실시간 철도안전 의사결정 지원시스템 개발), 김철홍
- 초록
- It may occur in the high-speed train many different types of failures such as support fixture crack, engine fault, composite train’s division/connection abnormality, axle’s rust, and shaking of its body. Such failures can threaten safe and reliable train operation. Sometimes some failure can cause failure of the other, and therefore discovering the association rules between various failures can help preventing the occurrence of related failures. In this paper, we propose to apply association rule mining for failure record data from a high-speed train.
- KSP 제안 키워드
- Association rule mining, Engine fault, High Speed Train, Support fixture, Train operation