DOI QR코드

DOI QR Code

A Basic Study on Structural Health Monitoring using the Kalman Filter

칼만 필터를 이용한 구조 안전성 모니터링에 관한 기초 연구

  • Park, Myong-Jin (Department of Naval Architecture and Ocean Engineering, INHA University) ;
  • Kim, Yooil (Department of Naval Architecture and Ocean Engineering, INHA University)
  • 박명진 (인하대학교 조선해양공학과) ;
  • 김유일 (인하대학교 조선해양공학과)
  • Received : 2020.11.28
  • Accepted : 2020.04.09
  • Published : 2020.06.20

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.

Keywords

References

  1. Cho, S., Park, J. W., Palanisamy. R. P. & Sim, S. H., 2016. Reference-free displacement estimation of bridges using Kalman filter-based nultimetric data fusion. Journal of Sensors, pp.1-9.
  2. Foss, G. C. & Haugse, E. D., 1995. Using modal test results to develop strain to displacement transformations, Proceedings of the 13th International Modal Analysis Conference, Nashville, Tennessee, USA, 13-16 February 1995, pp.112-118.
  3. Grewal, M. S. & Andrews, A. P., 2008. Kalman Filtering: Theory and Practice using MATLAB, 3rd Ed. Wiley-IEEE Press: New Jersey.
  4. Hamada, M.S., Wilson, A., Reese, C.S. & Martz, H.F., 2008. Bayesian Reliability. 1st Ed. Springer: New York.
  5. Hwang, J. S. & Kareem, A., 2007. Estimation of external loads using structural response. Journal of The Architectural Institute of KOREA Structure & Construction, 23(1), pp.51-61.
  6. Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), pp.35-45. https://doi.org/10.1115/1.3662552
  7. Papadimitriou, C., Fritzen, C. P., Kraemer, P. & Ntotsios, E., 2011. 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. https://doi.org/10.1002/stc.395
  8. Popov, I., Koschorrek, P., Haghani, A. & Jeinsch, T., 2017. Adaptive Kalman filtering for dynamic positioning of marine vessels. International Federation of Automatic Control-PapersOnLine, 50(1), pp.1121-1126.
  9. Rabiei, M. & Modarres, M., 2013. A recursive Bayesian framework for structural health management using online monitoring and periodic inspections, Reliability Engineering and System Safety, 112, pp.154-164. https://doi.org/10.1016/j.ress.2012.11.020
  10. Triantafyllou, M.A., Bodson, M. & Athans, M., 1983. Real time estimation of ship motions using Kalman filtering techniques, IEEE Journal of Ocean Engineering, 8(1), pp.9-20. https://doi.org/10.1109/JOE.1983.1145542