인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This paper is focused on estimation of rail irregularity with a mid-chord offset method from axle-box
accelerometers of an in-service high-speed train. In Korea, the rail condition monitoring has been done by a special railway inspection vehicle, EM-120. Unfortunately, the conventional monitoring method needs some contacting mechanical system which is expensive and it has speed limits to keep the contact. In this research, a rail irregularity estimation method is proposed to monitor the rail condition by some axle-box accelerometers of an in-service high-speed train. The axle-box acceleration was measured by vertical and lateral accelerometers on an axle-box, and stored in an on-board data acquisition system. The rail irregularity was estimated from the stored data by Kalman filter, frequency selective filter, and mid-chord offset filter. The estimated results were compared with the measurement result from a conventional railway inspection vehicle. From the comparison, the proposed method is able to apply in the rail condition monitoring by several in-service high-speed trains.
accelerometers of an in-service high-speed train. In Korea, the rail condition monitoring has been done by a special railway inspection vehicle, EM-120. Unfortunately, the conventional monitoring method needs some contacting mechanical system which is expensive and it has speed limits to keep the contact. In this research, a rail irregularity estimation method is proposed to monitor the rail condition by some axle-box accelerometers of an in-service high-speed train. The axle-box acceleration was measured by vertical and lateral accelerometers on an axle-box, and stored in an on-board data acquisition system. The rail irregularity was estimated from the stored data by Kalman filter, frequency selective filter, and mid-chord offset filter. The estimated results were compared with the measurement result from a conventional railway inspection vehicle. From the comparison, the proposed method is able to apply in the rail condition monitoring by several in-service high-speed trains.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2012-326-003878657
