DOI QR코드

DOI QR Code

Data fusion based improved Kalman filter with unknown inputs and without collocated acceleration measurements

  • Lei, Ying (Department of Civil Engineering, Xiamen University) ;
  • Luo, Sujuan (Department of Civil Engineering, Xiamen University) ;
  • Su, Ying (Department of Civil Engineering, Xiamen University)
  • 투고 : 2015.12.20
  • 심사 : 2016.05.22
  • 발행 : 2016.09.25

초록

The classical Kalman filter (KF) can provide effective state estimation for structural identification and vibration control, but it is applicable only when external inputs are measured. So far, some studies of Kalman filter with unknown inputs (KF-UI) have been proposed. However, previous KF-UI approaches based solely on acceleration measurements are inherently unstable which leads to poor tracking and fictitious drifts in the identified structural displacements and unknown inputs in the presence of measurement noises. Moreover, it is necessary to have the measurements of acceleration responses at the locations where unknown inputs applied, i.e., with collocated acceleration measurements in these approaches. In this paper, it aims to extend the classical KF approach to circumvent the above limitations for general real time estimation of structural state and unknown inputs without using collocated acceleration measurements. Based on the scheme of the classical KF, an improved Kalman filter with unknown excitations (KF-UI) and without collocated acceleration measurements is derived. Then, data fusion of acceleration and displacement or strain measurements is used to prevent the drifts in the identified structural state and unknown inputs in real time. Such algorithm is not available in the literature. Some numerical examples are used to demonstrate the effectiveness of the proposed approach.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China (NSFC)

참고문헌

  1. Ay, A.M. and Wang, Y. (2014), "Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion", Struct. Health Monit., 13(4), 445-460. https://doi.org/10.1177/1475921714542891
  2. Azam, S.E., Chatzi, E. and Costas Papadimitriou, C. (2015), "A dual Kalman filter approach for state estimation via output-only acceleration measurements", Mech. Syst. Signal Pr., 60-61, 866-886. https://doi.org/10.1016/j.ymssp.2015.02.001
  3. Chen, J. and Li, J. (2004). "Simultaneous identification of structural parameters and input time history from output-only measurements", Comput. Mech., 33(5), 365-374. https://doi.org/10.1007/s00466-003-0538-9
  4. Ding Y., Law S.S., Wu B., Xu G.S., Lin Q., Jiang H.B. and Miao Q.S. (2013), "Average acceleration discrete algorithm for force identification in the state space", Eng. Struct., 56,1880-1892. https://doi.org/10.1016/j.engstruct.2013.08.004
  5. Gillijns, S. and Moor, B.D. (2007), "Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feed through", Automatica, 43, 934-937. https://doi.org/10.1016/j.automatica.2006.11.016
  6. Huang, H.W., Yang, J.N. and Zhou, L. (2010), "Comparison of various structural damage tracking techniques based on experimental data", Smart Struct. Syst., 6(9), 1057-1077. https://doi.org/10.12989/sss.2010.6.9.1057
  7. Kalman, R.E. (1960), "A new approach to linear filtering and prediction problems", J. Basic Eng., 82(1), 35-45. https://doi.org/10.1115/1.3662552
  8. Kim, J., Kim, K. and Sohn, H. (2014), "Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements", Mech. Syst. Signal Pr., 42(1-2), 194-205. https://doi.org/10.1016/j.ymssp.2013.09.014
  9. Lei, Y., Liu C. and Liu, L.J. (2014), "Identification of multistory shear buildings under unknown earthquake excitation using partial output measurements: numerical and experimental studies", Struct. Control Health Monit., 21(5), 774-783.
  10. Lei, Y., Chen, F. and Zhou, H. (2015), "A two-stage and two-step algorithm for the identification of structural damage and unknown excitations: Numerical and experimental studies", Smart Struct. Syst., 15(1), 57-80. https://doi.org/10.12989/sss.2015.15.1.057
  11. Lei, Y., Jiang, Y.Q. and Xu Z.Q. (2012), "Structural damage detection with limited input and output measurement signals", Mech. Syst. Signal Pr., 28, 229-243. https://doi.org/10.1016/j.ymssp.2011.07.026
  12. Li, Y.Y. and Chen, Y. (2013), "A review on recent development of vibration-based structural robust damage detection", Struct. Eng. Mech., 45(2), 159-168. https://doi.org/10.12989/sem.2013.45.2.159
  13. Liu K., Law S.S., Zhu X.Q. and Xia Y. (2014), "Explicit form of an implicit method for inverse force identification", J. Sound Vib., 333, 730-744. https://doi.org/10.1016/j.jsv.2013.09.040
  14. Liu, J.J., Ma, C.K., Kung, I.C. and Lin, D.C. (2000),"Input force estimation of a cantilever plate by using a system identification technique", Comput. Method. Appl. Mech. Eng., 190, 1309-1322. https://doi.org/10.1016/S0045-7825(99)00465-X
  15. Liu, L.J., Su, Y., Zhu, J.J. and Lei, Y. (2016), "Improved Kalman filter with unknown inputs based on data fusion of partial acceleration and displacement measurements", Smart Struct. Syst., 17(6), 903-915. https://doi.org/10.12989/sss.2016.17.6.903
  16. Lourens, E., Reynders, E., Roeck, G. De, Degrande G. and Lombaert, G. (2012), "An augmented Kalman filter for force identification in structural dynamics", Mech. Syst. Signal Pr., 27, 446-460. https://doi.org/10.1016/j.ymssp.2011.09.025
  17. Ma, C.K., Chang J.M. and Lin D.C. (2003), "Input forces estimation of beam structures by an inverse method", J. Sound Vib., 259(2), 387-407 https://doi.org/10.1006/jsvi.2002.5334
  18. Naets, F., Cuadrado, J. and Desmet, W. (2015), "Stable force identification in structural dynamics using Kalman filtering and dummy-measurements", Mech. Syst. Signal Pr., 50-51, 235-248. https://doi.org/10.1016/j.ymssp.2014.05.042
  19. Pan, S.W., Su, H.Y., Wang, H. and Chu, J. (2010), "The study of input and state estimation with Kalman filtering", Inst. Measure. Control, 33(8), 901-918.
  20. Papadimitriou, C., Fritzen, C.P., Kraemer, P. and Ntotsios, E. (2011), "Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering", Struct. Control Health Monit., 18(5), 554-573. https://doi.org/10.1002/stc.395
  21. Sirca, G.F., Jr. and Adeli, H. (2012), "System identification in structural engineering", Scientia Iranica-Transaction A: Civil Eng., 19(6), 1355-1364. https://doi.org/10.1016/j.scient.2012.09.002
  22. Smyth, A. and Wu, M. (2007), "Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring", Mech. Syst. Signal Pr., 21, 706-723. https://doi.org/10.1016/j.ymssp.2006.03.005
  23. Wang, Y., Lynch, J.P. and Law, K.H. (2009), "Decentralized H-infinity controller design for large-scale civil structures", Earthq. Eng. Struct. D., 38(3), 377-401. https://doi.org/10.1002/eqe.862
  24. Wu, A.L., Loh, C.H., Yang J.N., Weng, J.H., Chen, C.H. and Ueng T.S. (2009), "Input force identification:application to soil-pile interaction", Struct. Control Health Monit., 16, 223-240. https://doi.org/10.1002/stc.308
  25. Xu, B. and He, J. (2015), "Substructural parameters and dynamic loading identification with limited observations", Smart Struct. Syst., 15(1), 169-189. https://doi.org/10.12989/sss.2015.15.1.169
  26. Xu, B., He, J., Rovekamp, R. and Dyke, S.J. (2012), "Structural parameters and dynamic loading identification from incomplete measurements: Approach and validation", Mech. Syst. Signal Pr., 28, 244-257. https://doi.org/10.1016/j.ymssp.2011.07.008
  27. Yi, T.H., LI, H.N. and Wang, X. (2013), "Multi-dimensional sensor placement optimization for Canton Tower focusing on application demands", Smart Struct. Syst., 12(3-4), 235-250. https://doi.org/10.12989/sss.2013.12.3_4.235
  28. Yuen, K.V. and Mu, H.Q. (2015), "Real-time system identification: An algorithm for simultaneous model class selection and parametric identification", Comput. - Aided Civil Infrastruct. Eng., 30(10), 785-801. https://doi.org/10.1111/mice.12146
  29. Yuen, K.V., Liang, P.F. and Kuok, S.C. (2013), "Online estimation of noise parameters for Kalman filter", Struct. Eng. Mech., 47(3), 361-381. https://doi.org/10.12989/sem.2013.47.3.361

피인용 문헌

  1. A sensor fault detection strategy for structural health monitoring systems vol.20, pp.1, 2016, https://doi.org/10.12989/sss.2017.20.1.043
  2. Effective Heterogeneous Data Fusion procedure via Kalman filtering vol.22, pp.5, 2018, https://doi.org/10.12989/sss.2018.22.5.631
  3. Investigations on state estimation of smart structure systems vol.25, pp.1, 2020, https://doi.org/10.12989/sss.2020.25.1.037
  4. A general synthesis of identification and vibration control of building structures under unknown excitations vol.143, pp.None, 2016, https://doi.org/10.1016/j.ymssp.2020.106803
  5. Identification of the nonlinear characteristics of rubber bearings in model-free base-isolated buildings using partial measurements of seismic responses vol.39, pp.3, 2016, https://doi.org/10.1177/1461348419843385