Estimation of hysteretic damping of structures by stochastic subspace identification

Abstract Output-only system identification techniques can estimate modal parameters of structures represented by linear time-invariant systems. However, the extension of the techniques to structures exhibiting non-linear behavior has not received much attention. This paper presents an output-only system identification method suitable for random response of dynamic systems with hysteretic damping. The method applies the concept of Stochastic Subspace Identification (SSI) to estimate the model parameters of a dynamic system with hysteretic damping. The restoring force is represented by the Bouc-Wen model, for which an equivalent linear relaxation model is derived. Hysteretic properties can be encountered in engineering structures exposed to severe cyclic environmental loads, as well as in vibration mitigation devices, such as Magneto-Rheological (MR) dampers. The identification technique incorporates the equivalent linear damper model in the estimation procedure. Synthetic data, representing the random vibrations of systems with hysteresis, validate the estimated system parameters by the presented identification method at low and high-levels of excitation amplitudes.

[1]  Y. Wen Equivalent Linearization for Hysteretic Systems Under Random Excitation , 1980 .

[2]  Guido De Roeck,et al.  Fully automated (operational) modal analysis , 2012 .

[3]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[4]  Shirley J. Dyke,et al.  PHENOMENOLOGICAL MODEL FOR MAGNETORHEOLOGICAL DAMPERS , 1997 .

[5]  Eleni Chatzi,et al.  Experimental application of on-line parametric identification for nonlinear hysteretic systems with model uncertainty , 2010 .

[6]  Wim Desmet,et al.  Stable force identification in structural dynamics using Kalman filtering and dummy-measurements , 2015 .

[7]  M. Ghandchi Tehrani,et al.  Nonlinear damping and quasi-linear modelling , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  R. Narayana Iyengar,et al.  Higher order linearization in non-linear random vibration , 1988 .

[9]  Thanh Nho Do,et al.  A survey on hysteresis modeling, identification and control , 2014 .

[10]  Keith Worden,et al.  On the identification of hysteretic systems. Part I: Fitness landscapes and evolutionary identification , 2012 .

[11]  James L. Beck,et al.  Structural identification using linear models and earthquake records , 1980 .

[12]  Costas Papadimitriou,et al.  Experimental validation of the Kalman-type filters for online and real-time state and input estimation , 2017 .

[13]  C. Papadimitriou,et al.  A dual Kalman filter approach for state estimation via output-only acceleration measurements , 2015 .

[14]  Sondipon Adhikari,et al.  Qualitative dynamic characteristics of a non-viscously damped oscillator , 2005, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  Gerd Vandersteen,et al.  Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets , 1997, Autom..

[16]  Mohammed Ismail,et al.  The Hysteresis Bouc-Wen Model, a Survey , 2009 .

[17]  T. Caughey Equivalent Linearization Techniques , 1962 .

[18]  Keith Worden,et al.  On the identification of hysteretic systems. Part II: Bayesian sensitivity analysis and parameter confidence , 2012 .

[19]  Jan Becker Høgsberg,et al.  Evaluation of damping estimates by automated Operational Modal Analysis for offshore wind turbine tower vibrations , 2018 .

[20]  J. P. Noël,et al.  Hysteretic benchmark with a dynamic nonlinearity , 2016 .

[21]  Kalil Erazo,et al.  A model-based observer for state and stress estimation in structural and mechanical systems: Experimental validation , 2014 .

[22]  Karl Johan Åström,et al.  Numerical Identification of Linear Dynamic Systems from Normal Operating Records , 1965 .

[23]  Daniel Bedoya-Ruiz,et al.  Identification of Bouc-Wen type models using the Transitional Markov Chain Monte Carlo method , 2015 .

[24]  Edwin Reynders,et al.  System Identification Methods for (Operational) Modal Analysis: Review and Comparison , 2012 .

[25]  F. Ikhouane,et al.  Systems with Hysteresis: Analysis, Identification and Control Using the Bouc-Wen Model , 2007 .

[26]  Wim Desmet,et al.  Online state and input force estimation for multibody models employing extended Kalman filtering , 2014 .

[27]  Richard W. Longman,et al.  On‐line identification of non‐linear hysteretic structural systems using a variable trace approach , 2001 .

[28]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[29]  W. Hume-rothery Elasticity and Anelasticity of Metals , 1949, Nature.

[30]  Adrian C. Orifici,et al.  Fully Automated Operational Modal Analysis using multi-stage clustering , 2017 .

[31]  Dionisio Bernal,et al.  Kalman filter damage detection in the presence of changing process and measurement noise , 2013 .

[32]  P. Spanos,et al.  Random vibration and statistical linearization , 1990 .

[33]  W. Desmet,et al.  An online coupled state/input/parameter estimation approach for structural dynamics , 2015 .

[34]  Guido De Roeck,et al.  REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS , 1999 .

[35]  James L. Beck,et al.  Real-time Reliability Estimation for Serviceability Limit States in Structures with Uncertain Dynamic Excitation and Incomplete Output Data , 2007 .

[36]  Sondipon Adhikari,et al.  Dynamics of Nonviscously Damped Linear Systems , 2002 .

[37]  Johan Schoukens,et al.  A nonlinear state-space approach to hysteresis identification , 2016, ArXiv.

[38]  Y. Wen Method for Random Vibration of Hysteretic Systems , 1976 .

[39]  S. N. Voormeeren,et al.  Vibration-based Identification of Hydrodynamic Loads and System Parameters for Offshore Wind Turbine Support Structures☆ , 2016 .

[40]  N. N. Bogoli︠u︡bov,et al.  Introduction to non-linear mechanics , 1943 .

[41]  J. Hensman,et al.  Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference , 2012 .

[42]  Andrew W. Smyth,et al.  On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems , 1999 .