칼만 필터를 이용한 구조 안전성 모니터링에 관한 기초 연구
A Basic Study on Structural Health Monitoring using the Kalman Filter
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.
Abstract
For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.
참고문헌 (10)
- Cho, S., "Reference-free displacement estimation of bridges using Kalman filter-based nultimetric data fusion", Journal of Sensors, pp.1~9 (2016)
- Foss, G. C., "Using modal test results to develop strain to displacement transformations", Proceedings of the 13th International Modal Analysis Conference, pp.112~118 (1995)
- Grewal, M. S., "Kalman Filtering: Theory and Practice using MATLAB" (2008), Wiley-IEEE Press
- Hamada, M. S., "Bayesian Reliability" (2008), Springer
- 황재승, "구조물 응답을 이용한 외부하중 역 추정 기법에 관한 연구", 대한건축학회논문집 구조계, 23(1), pp.51~61 (2007), 대한건축학회
- Kalman, R. E., "A new approach to linear filtering and prediction problems", Journal of Basic Engineering, 82(1), pp.35~45 (1960)
- Papadimitriou, C., "Fatigue predictions in entier body of metallic structures from a limited number of vibration sensors using Kalman filtering", Structural Control and Health Monitoring, 18, pp.554~573 (2011)
- Popov, I., "Adaptive Kalman filtering for dynamic positioning of marine vessels", International Federation of Automatic Control-PapersOnLine, 50(1), pp.1121~1126 (2017)
- Rabiei, M., "A recursive Bayesian framework for structural health management using online monitoring and periodic inspections", Reliability Engineering and System Safety, 112, pp.154~164 (2013)
- Triantafyllou, M. A., "Real time estimation of ship motions using Kalman filtering techniques", IEEE Journal of Ocean Engineering, 8(1), pp.9~20 (1983)