DOI QR코드

DOI QR Code

Application of recursive SSA as data pre-processing filter for stochastic subspace identification

  • Loh, Chin-Hsiung (Department of Civil Engineering, National Taiwan University) ;
  • Liu, Yi-Cheng (Department of Civil Engineering, National Taiwan University)
  • Received : 2012.03.22
  • Accepted : 2013.01.09
  • Published : 2013.01.25

Abstract

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.

Acknowledgement

Supported by : National Science Council, Taiwan

References

  1. Alonso, F.J., Del Castillo, J.M. and Pintado, P. (2005), "Application of singular spectrum analysis to the smoothing of raw kinematic signals", J. Biomech., 38(5), 1085-1092. https://doi.org/10.1016/j.jbiomech.2004.05.031
  2. Bart, P. and Guido, D.R. (1999), "Reference-based stochastic subspace identification for output-only modalanalysis", Mech. Syst. Signal Pr., 13(6) 855-878.
  3. Bart, P. (2000), System Identification and Damage Detection in Civil Engineering, Ph.D. Dissertation, Katholieke Universiteit, Leuven, December.
  4. Caicedo, J.M., Dyke, S.J. and Johnson, E.A. (2004), "Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: simulated data", J. Eng. Mech.-ASCE, 130(1), 49-60. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(49)
  5. Goethals, I., Mevel, L., Benveniste, A. and De Moor, B. (2004),"Recursive output only subspace identification for in-flight flutter monitoring", Proceedings of the 22nd International Modal Analysis Conference, Dearborn, Michigan.
  6. Golub, G.H. and Van Loan, C.F. (1996), Matrix computations, Johns Hopkins University Press.
  7. Loh, C.H., Mao, C.H., Chao, S.H. and Weng, J.H. (2011), "System identification and damage evaluation of degrading hysteresis of reinforced concrete frames", Earthq. Eng. Struct. D., 40(6), 623-640. https://doi.org/10.1002/eqe.1051
  8. Loh, C.H., Weng, J.H., Liu, Y.C., Lin, P.Y. and Huang, S.K. (2011), "Structural damage diagnosis based on on-line recursive stochastic subspace identification", Smart Mater. Struct., 20(5), 055004 (10pp). https://doi.org/10.1088/0964-1726/20/5/055004
  9. Michele B., Albert, B., Maurice, G., Luc, H., Laurent, M. and Herman, V.D.A. (2001), "Output-only subspace-based structural identification: From theory to industrial testing practice", J. Dyn. Syst. Measure.Control, 123(4), 668-676. https://doi.org/10.1115/1.1410919
  10. Soderstrom, T. and Stoica, P. (1989), System identification, Prentice-Hall International.
  11. Van Overschee, P. and De Moor, B. (1996), Subspace Identification for Linear Systems: theory - Implementation - Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands.
  12. Weng, J.H., Loh, C.H., Lynch, J.P., Lu, K.C., Linn, P.Y. and Wang, Y. (2008), "Output-only modal identification of a cable-stayed bridge using wireless monitoring systems", J. Eng. Struct., 30(2), 1802-1830. https://doi.org/10.1016/j.engstruct.2007.12.004
  13. Yang, B. (1995), "Projection approximation subspace tracking", IEEE T. Signal Process., 43(1), 95-107. https://doi.org/10.1109/78.365290

Cited by

  1. Automated data interpretation for practical bridge identification vol.46, pp.3, 2013, https://doi.org/10.12989/sem.2013.46.3.433
  2. Near-Real-Time Hybrid System Identification Framework for Civil Structures with Application to Burj Khalifa vol.142, pp.2, 2016, https://doi.org/10.1061/(ASCE)ST.1943-541X.0001402
  3. Vibration-based system identification of wind turbine system vol.24, pp.3, 2017, https://doi.org/10.1002/stc.1876