Abstract
Among numerous damage identification techniques, those which are used for online data-driven damage identification have received considerable attention recently. One of the most widely-used vibration-based time-domain techniques for nonlinear system identification is the extended Kalman filter, which exhibits a good performance when the parameter to be identified is a constant parameter. However, it is not as successful in identification of changes in time-varying system parameters, which is essential for real-time identification. Alternatively, the extended Kalman-Bucy filter has been recently proposed due to its enhanced capabilities in parameter estimation compared with extended Kalman filter. On the other hand, when applied as the excitation in some attractor-based damage identification techniques, chaotic and hyperchaotic dynamics produce better outcomes than does common stochastic white noise. The current study combines hyperchaotic excitations and the enhanced capabilities of extended Kalman-Bucy filter to propose a real-time approach for identification of damage in nonlinear structures. Simulation results show that the proposed approach is capable of online identification and assessment of damage in nonlinear elastic and hysteretic structures with single or multiple degrees-of-freedom using noise-corrupted measured acceleration response.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lin J, Betti R, Smyth AW (2001) Online identification of nonlinear hysteretic structural system using a variable trace approach. Earthq Eng Struct Dyn 30:1279–1303
Smyth AW, Masri SF, Chassiakos AG, Caughey TK (1999) Online parametric identification of MDOF nonlinear hysteretic systems. ASCE J Eng Mech 125:133–142
Yang JN, Lin S (2004) Online identification of nonlinear hysteretic structures using an adaptive tracking technique. Int J Nonlinear Mech 39:1481–1491
Shinozuka M, Yun C, Imai H (1982) Identification of linear structural dynamic systems. ASCE J Eng Mech 108:1371–1390
Shinozuka M, Ghanem R (1995) Structural system identification. II: experimental verification. ASCE J Eng Mech 121:265–273
Ghanem RG, Shinozuka M (1995) Structural-system identification. I: theory. ASCE J Eng Mech 121(2):255–264
Hoshiya M, Saito E (1984) Structural identification by extended Kalman filter. ASCE J Eng Mech 110:1757–1771
Sato T, Qi K (1998) Adaptive H ∞ filter: its application to structural identification. ASCE J Eng Mech 124(11):1233–1240
Yoshida I (2001) Damage detection using Monte Carlo filter based on non-Gaussian noise. In: Proceedings of structural safety and reliability, ICOSSA 2001. Swet & Zeitinger, Lisse, 8 pp
Yang JN, Lin S, Huang H, Zhou L (2006) An adaptive extended Kalman filter for structural damage identification. Struct Control Health Monit 13:849–867
Loh CH, Lin CY, Huang CC (2000) Time domain identification of frames under earthquake loadings. ASCE J Eng Mech 126(7):693–703
Loh CH, Tou IC (1995) A system identification approach to the detection of changes in both linear and nonlinear structural parameters. J Earthq Eng Struct Dyn 24:85–97
Åström KJ (1980) Self-tunning regulators: design principles and applications. In: Narendra KS, Monopoli RV (eds) Applications of adaptive control. Academic, New York, pp 1–68
Torkamani S (2013) Hyperchaotic and delayed oscillators for system identification with application to damage assessment. PhD dissertation, New Mexico State University
Nichols JM, Trickey ST, Todd MD, Virgin LN (2003) Structural health monitoring through chaotic interrogation. Meccanica 38:239–250
Nichols JM, Todd MD, Virgin LN, Nichols JD (2003) On the use of attractor dimension as a feature in structural health monitoring. Mech Syst Signal Process 17:1305–1320
Olson C, Todd MD, Worden K, Farrar C (2007) Improving excitations for active sensing in structural health monitoring via evolutionary algorithms. J Vib Acoust 129:784–802
Olson CC, Todd MD (2010) On the convergence of multiple excitation sources to a global optimum excitation in active sensing for structural health monitoring. Struct Control Health Monit 17:23–47
Olson CC, Overbey LA, Todd MD (2009) The effect of detection feature type on excitations bred for active sensing in structural health monitoring. J Intell Mater Struct Syst 20:1307–1327
Torkamani S, Butcher EA, Todd MD, Park GP (2011) Hyperchaotic probe for damage identification using nonlinear prediction error. Mech Syst Signal Process 29:457–473
Torkamani S, Butcher EA, Todd MD, Park GP (2011) Detection of system changes due to damage using a tuned hyperchaotic probe. Smart Mater Struct 20:025006
Jazwinski AH (1970) Stochastic processes and filtering theory. Academic, New York
Guosi Hu (2009) Generating hyperchaotic attractors with three positive Lyapunov exponents via state feedback control. Int J Bifurcation Chaos 19:651–660
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 The Society for Experimental Mechanics
About this paper
Cite this paper
Torkamani, S., Butcher, E.A., Todd, M.D. (2014). Real-Time Damage Identification in Nonlinear Smart Structures Using Hyperchaotic Excitation and Stochastic Estimation. In: Allemang, R., De Clerck, J., Niezrecki, C., Wicks, A. (eds) Topics in Modal Analysis, Volume 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6585-0_27
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6585-0_27
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6584-3
Online ISBN: 978-1-4614-6585-0
eBook Packages: EngineeringEngineering (R0)