Stochastic change detection in uncertain nonlinear systems using reduced-order models: system identification

The reliable detection of relatively small changes in the characteristics of monitored systems, which simultaneously involve nonlinear phenomena as well as uncertain parameters, is a challenging problem whose resolution is crucial to the development of practical structural health monitoring methodologies slated for use with complex physical systems. This paper reports the results of a comprehensive experimental study involving an adaptive nonlinear component (an actively controlled magnetorheological damper) that was used to investigate the representation and propagation of uncertainties in a probabilistic format that provides a convenient means for reliable detection of small changes in uncertain nonlinear systems. In experimental studies of the MR damper, the uncertainty of the system characteristics was precisely controlled with known input-current statistics. A total of 4000 tests were performed, and the MR damper was identified using the restoring force method with both orthogonal and non-orthogonal basis functions. The identification results show that the identified coefficients involving orthogonal basis functions have several desirable features that are ideal for condition assessment purposes when dealing with complex nonlinear systems whose underlying physics is not amenable to easy modeling: (1) no a priori knowledge of the systems characteristics is required; (2) the orthogonal coefficients are statistically unbiased with respect to model complexity; and (3) the distributions of the orthogonal coefficients can be reliably used to detect changes in uncertain nonlinear systems, even if a reduced-order model is used in the identification process.

[1]  Bart Peeters,et al.  International Research Projects on Structural Health Monitoring: An Overview , 2003 .

[2]  Keith Worden,et al.  Nonlinearity in Structural Dynamics , 2019 .

[3]  G. W. Housner.  Special issue : Structural control : Past, present, and future , 1997 .

[4]  Roger Ghanem,et al.  Stochastic nonparametric models of uncertain hysteretic oscillators , 2006 .

[5]  Billie F. Spencer,et al.  On the current status of magnetorheological dampers: seismic protection of full-scale structures , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[6]  T. T. Soong,et al.  STRUCTURAL CONTROL: PAST, PRESENT, AND FUTURE , 1997 .

[7]  S. Masri,et al.  Training neural networks by adaptive random search techniques , 1999 .

[8]  Akira Mita,et al.  Emerging Needs in Japan for Health Monitoring Technologies in Civil and Building Structures , 1999 .

[9]  Nicos Makris,et al.  Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers , 2008 .

[10]  Siak Piang Lim,et al.  Contact modeling of viscoelastic friction layer of traveling wave ultrasonic motors , 2001 .

[11]  Alan J. Lee,et al.  Linear Regression Analysis: Seber/Linear , 2003 .

[12]  Sami F. Masri,et al.  Modeling the oscillatory dynamic behaviour of electrorheological materials in shear , 1992 .

[13]  Sami F. Masri,et al.  A Nonparametric Identification Technique for Nonlinear Dynamic Problems , 1979 .

[14]  Billie F. Spencer,et al.  Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction , 1996 .

[15]  S. Masri,et al.  Application of Neural Networks for Detection of Changes in Nonlinear Systems , 2000 .

[16]  B. Peeters,et al.  Vibration-based damage detection in civil engineering: excitation sources and temperature effects , 2001 .

[17]  S. Masri,et al.  Identification of Nonlinear Dynamic Systems Using Neural Networks , 1993 .

[18]  Sami F. Masri,et al.  Some structural health monitoring approaches for nonlinear hydraulic dampers , 2002 .

[19]  Billie F. Spencer,et al.  Dynamic Modeling of Large-Scale Magnetorheological Damper Systems for Civil Engineering Applications , 2004 .