Novel Unscented Kalman Filter for Health Assessment of Structural Systems with Unknown Input
Publication: Journal of Engineering Mechanics
Volume 141, Issue 7
Abstract
A novel procedure for structural health assessment, denoted as unscented Kalman filter with unknown input (UKF-UI), is proposed using the nonlinear system identification concept. To increase its implementation potential, a substructure concept is introduced, producing a two-stage approach. It integrates the unscented Kalman filter concept and an iterative least-squares technique. The two most important features of the method are that it does not need the information on the time history of the excitation to identify structural systems represented by finite elements, and that it can identify defects in them using only a limited amount of noise-contaminated nonlinear response information. The proposed method is robust enough to detect the locations and severity of defects at different locations in the structure. The defect detection capability increases significantly if the defective member is in the substructure or close to it. The method is conclusively verified with the help of two examples using impulsive and seismic excitations. The superiority of UKF-UI over extended Kalman filter-based procedures is documented. The proposed UKF-UI procedure has high implementation potential and can be used for health assessment of large structural systems.
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Acknowledgments
This study is based on work partly supported by the Iraq’s Ministry of Higher Education and Scientific Research. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the writers and do not necessarily reflect the views of the sponsor.
References
ANSYS 14.0 [Computer software]. Canonsburg, PA, Engineering Solutions.
ASCE. (2013). “Report card for America’s infrastructure.” 〈http://www.infrastructurereportcard.org〉 (Nov. 11, 2013).
Chatzi, E. N., and Smyth, A. W. (2009). “The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing.” Struct. Control Health Monit., 16(1), 99–123.
Chatzi, E. N., Smyth, A. W., and Masri, S. F. (2010). “Experimental application of on-line parametric identification for nonlinear hysteretic systems with model uncertainty.” J. Struct. Saf., 32(5), 326–337.
Clough, R. W., and Penzien, J. (2003). Dynamics of structures, 3 rd Ed., Computers and Structures, Berkeley, CA.
Cook, R. D., Malkus, D. S., Plesha, M. E., and Witt, R. J. (2002). Concepts and applications of finite element analysis, 4th Ed., Wiley, Hoboken, NJ.
Das, A. K. (2012). “Health assessment of three dimensional large structural systems using limited uncertain dynamic response information.” Ph.D. dissertation, Univ. of Arizona, Tucson, AZ.
Das, A. K., Haldar, A., and Chakraborty, S. (2012). “Health assessment of large two dimensional structures using limited information—Recent advances.” Adv. Civ. Eng., 2012, 1–16.
Doebling, S. W., Farrar, C. R., Prime, M. B., and Shevitz, D. W. (1996). “Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review.”, Los Alamos National Laboratory, Los Alamos, NM.
Hao, Y., Xiong, Z., Sun, F., and Wang, X. (2007). “Comparison of unscented Kalman filters.” Proc., IEEE Int. Conf. Mechatronics Automation, Harbin, China, 895–899.
Hoshiya, M., and Saito, E. (1984). “Structural identification by extended Kalman filter.” J. Eng. Mech., 1757–1770.
Jazwinski, A. H. (1970). Stochastic processes and filtering theory, Academic Press, New York.
Julier, S. J., and Uhlmann, J. K. (1997). “A new extension of the Kalman filter to nonlinear systems.” Proc., AeroSense: 11th Int. Symp. on Aerospace/Defense Sensing, Simulation and Controls, SPIE, Orlando, FL, 182–193.
Julier, S. J., Uhlmann, J. K., and Durrant-Whyte, H. F. (1995). “A new approach for filtering nonlinear systems.” Proc., American Control Conf., Seattle, Washington, 1628–1632.
Katkhuda, H. (2004). “In-service health assessment of real structures at the element level with unknown input and limited global responses.” Ph.D. dissertation, Univ. of Arizona, Tucson, AZ.
Katkhuda, H., and Haldar, A. (2008). “A novel health assessment technique with minimum information.” Struct. Control Health Monit., 15(6), 821–838.
Katkhuda, H., Martinez-Flores, R., and Haldar, A. (2005). “Health assessment at local level with unknown input excitation.” J. Struct. Eng., 956–965.
Kerschen, G., Worden, K., Vakakis, A. F., and Golinval, J. C. (2006). “Past, present and future of nonlinear system identification in structural dynamics.” Mech. Syst. Sig. Process., 20(3), 505–592.
Koh, C. G., See, L. M., and Balendra, T. (1991). “Estimation of structural parameters in time domain: A substructural approach.” Earthquake Eng. Struct. Dyn., 20(8), 787–801.
Ling, X., and Haldar, A. (2004). “Element level system identification with unknown input with Rayleigh damping.” J. Eng. Mech., 877–885.
Mariani, S., and Ghisi, A. (2007). “Unscented Kalman filtering for nonlinear structural dynamics.” Nonlinear Dyn., 49(1), 131–150.
Maybeck, P. S. (1979). Stochastic models, estimation, and control theory, Chapter 1, Academic Press, London, U.K., 1–2.
Sohn, H., et al. (2004). “A review of structural health monitoring literature: 1996–2001.”, Los Alamos National Laboratory, Los Alamos, NM.
Toki, K., Sato, T., and Kiyono, J. (1989). “Identification of structural parameters and input ground motion from response time histories.” Struct. Eng. Earthquake Eng., 6(2), 413–421.
Vo, P., and Haldar, A. (2003). “Post processing of linear accelerometer data in structural identification.” J. Struct. Eng., 30(2), 123–130.
Wan, E. A., and van der Merwe, R. (2000). “The unscented Kalman filter for nonlinear estimation.” Proc., Symp. Adaptive Systems for Signal Processing, Communications, and Control, IEEE, New York.
Wang, D., and Haldar, A. (1994). “Element-level system identification with unknown input.” J. Eng. Mech., 159–176.
Wang, D., and Haldar, A. (1997). “System identification with limited observations and without input.” J. Eng. Mech., 504–511.
Wu, M., and Smyth, A. W. (2007). “Application of the unscented Kalman filter for real-time nonlinear structural system identification.” Struct. Control Health Monit., 14(7), 971–990.
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© 2015 American Society of Civil Engineers.
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Received: Nov 11, 2013
Accepted: Jan 5, 2015
Published online: Apr 22, 2015
Published in print: Jul 1, 2015
Discussion open until: Sep 22, 2015
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