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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.21 No.5
- 발행연도
- 2018.1
- 수록면
- 705 - 713 (9page)
이용수
초록· 키워드
오류제보하기
High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train’s operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.
목차
등록된 정보가 없습니다.
참고문헌 (55)
참고문헌 신청
Alexandrou, G. / 2016 / A comprehensive prediction model for vehicle/track/soil dynamic response due to wheel flats / Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit 230(4):1088~1104
Barke, D.W. / 2005 / A review of the effects of out-of-round wheels on track and vehicle components / Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit 219(3):151~175
Barke, D.W. / 2005 / Structural health monitoring in the railway industry: A review / Struct. Health Monit. 4(1):81~93
Bian, J. / 2013 / Numerical study of impact forces on railway sleepers under wheel flat / Adv. Struct. Eng. 16(1):127~134
Bornn, L. / 2009 / Structural health monitoring with autoregressive support vector machines / J. Vib. Acoust., 131(2):021004
Barke, D.W. / 2005 / A review of the effects of out-of-round wheels on track and vehicle components / Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit 219(3):151~175
Barke, D.W. / 2005 / Structural health monitoring in the railway industry: A review / Struct. Health Monit. 4(1):81~93
Bian, J. / 2013 / Numerical study of impact forces on railway sleepers under wheel flat / Adv. Struct. Eng. 16(1):127~134
Bornn, L. / 2009 / Structural health monitoring with autoregressive support vector machines / J. Vib. Acoust., 131(2):021004
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