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Although the finite element method is often applied to analyze the dynamics of structures, its application to large, complex structures can be time-consuming and errors in the modeling process may negatively affect the accuracy of analyses based on the model. System identification techniques attempt to circumvent these problems by using experimental response data to characterize or identify a system. However, identification of structures that are time-varying or nonlinear is problematic because the available methods generally require prior understanding about the equations of motion for the system. Nonlinear system identification techniques are generally only applicable to nonlinearities where the functional form of the nonlinearity is known and a general nonlinear system identification theory is not available as is the case with linear theory. Linear time-varying identification methods have been proposed for application to nonlinear systems, but methods for general time-varying systems where the form of the time variance is unknown have only been available for single-input single-output models. This dissertation presents several general linear time-varying methods for multiple-input multiple-output systems where the form of the time variance is entirely unknown. The methods use the proper orthogonal decomposition of measured response data combined with linear system theory to construct a model for predicting the response of an arbitrary linear or nonlinear system without any knowledge of the equations of motion. Separate methods are derived for predicting responses to initial displacements, initial velocities, and forcing functions. Some methods require only one data set but only promise accurate solutions for linear, time-invariant systems that are lightly damped and have a mass matrix proportional to the identity matrix. Other methods use multiple data sets and are valid for general time-varying systems. The proposed methods are applied to linear timeinvariant, time-varying, and nonlinear systems via numerical examples and experiments and the factors affecting the accuracy of the methods are discussed.
Journal of Sound and Vibration, 1991
Most of the modern techniques for vibration analysis are based on the modal approach. However, when the systems under study are not lightly damped, so that there are significant cross-damping terms in the governing equations, when there are closely spaced natural frequencies or when non-linearities exist, then the modal approach is extremely limited. A number of techniques have been proposed to deal with these problems, but the methods tend to produce models which are subjective. In this paper, a generalized non-iterative approach in which spectral techniques are used is presented for the identification of multi-degree-of-freedom systems from input/output records. The method is suitable for the analysis not only of systems with significant cross-damping but also of highly coupled non-linear systems. The implementation of the technique is illustrated with two numerically simulated examples.
The present work presents a time domain based identification technique within the inverse problems scope. Here the set of unknown parameters which characterizes the mechanical behaviour of the system is identified by means of the minimization of a suitable error function which includes time domain data from both the real system and its respective mathematical model. The technique takes into account the constraint associated to the system evolution equations as being part of an extended error function what naturally gives rise to the Lagrange multiplier variables which are obtained via solution of an adjoint problem. Mechanical modelling plays a crucial role here inasmuch as once one has decided which unknown parameters influence the system response, they are used to parameterize the mass, stiffness and damping matrices of the system. The effectiveness of the technique is assessed by using experimental data which was collected from a pinned-pinned steel beam instrumented with four piezoelectric accelerometers and an electro-mechanical shaker. The parameters which have been chosen to be identified were: localized damping at the bearings, the first three damping factors of the structure and a localized mass associated to the interaction between the structure and the shaker.
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2011
A review of current efforts towards developing a non-linear system identification (NSI) methodology of broad applicability is provided in this article. NSI possess distinct challenges, since, even the task of identifying a set of (linearized) modal matrices modified ('perturbed') by non-linear corrections might be an oversimplification of the problem. In that context, the integration of diverse analytical, computational, and post-processing methods, such as slow flow constructions, empirical mode decompositions, and wavelet/Hilbert transforms to formulate a methodology that holds promise of broad availability, especially to systems with non-smooth non-linearities such as clearances, dry friction and vibro-impacts is proposed. In particular, the proposed methodology accounts for the fact that, typically, non-linear systems are energy-and initial condition-dependent, and has both global and local components. In the global aspect of NSI, the dynamics is represented in a frequency-energy plot (FEP), whereas in the local aspect of the methodology, sets of intrinsic modal oscillators are constructed to model specific non-linear transitions on the FEP. The similarity of the proposed methodology to linear experimental modal analysis is discussed, open questions are outlined, and some applications providing a first demonstration of the discussed concepts and techniques are provided.
The aim of this paper is to present a numerical scheme for the identification of the nonlinear characteristics of a dynamically excited, single degree of freedom structure, using a non-parametric procedure, recently proposed by the second author; this involves the simultaneous identification of the nonlinear characteristics of both damping and restoring force in dynamic systems whose damping depends on velocity alone. According to this method, the response of the structure is first measured then an integral equation accounting for its unknown nonlinear characteristics is derived. This is an integral equation of the first kind, involving numerical instability in the Hadamard sense. To overcome this difficulty, the Landweber regularization, combined with the L-curve criterion, is applied to the integral equation. Adopting a dynamic model for a test structure, the corresponding nonlinear system identification is achieved through the proposed numerical solution of the governing integral...
Mechanical Systems and Signal Processing, 1989
Identification of mechanical elastic systems under dynamic actions is studied in a time-domain context. It is shown that the behaviour of such systems can be modelled with difference equations which reduce to a very simple form when one-input or free vibration cases are considered. Statistical analysis of error terms leads to well known models like ARMAX, ARMA and AR. The coefficients of these models are evaluated by two approaches, namely the method of least squares and the method of approximate maximum likelihood; the former is more suitable for free vibrations and the latter for forced vibration case. The eigenvalues and eigenvectors of the system are then evaluated from identified ARMAX, ARMA or AR models. Useful information on the actual performance of the methods is acquired by application of the derived algorithms for identification of modal values from artificial and experimental data. 157
IFAC Proceedings Volumes, 1985
A method for the parameter identification of linear, time-invariant systems under stationary, gaussian coloured excitation is presented. The input-and output signals of the considered system are processed in known linear filters. Stationary covariance relations between these signals allow identification of unknown system parameters. The method is developed in detail for mechanical multibody systems and sing~e-input single-output systems , , 40rds. Identification; covariance methods; continuous time systems; measurement noise; instrumental variable methods; mechanical multibody systems; singleinput single-output systems. I.
1995
This dissertation considers the identi cation of linear multivariable systems using nite dimensional time-invariant state-space models. Parametrization of multivariable state-space models is considered. A full parametrization, where all elements in the state-space matrices are parameters, is introduced. A model structure with full parametrization gives two important implications; low sensitivity realizations can be used and the structural issues of multivariable canonical parametrizations are circumvented. Analysis reveals that additional estimated parameters do not increase the variance of the transfer function estimate if the resulting model class is not enlarged. Estimation and validation issues for the case of impulse response data are discussed. Identi cation techniques based on realization theory are linked to the prediction error method. The combination of these techniques allows for the estimation of high quality models for systems with many oscillative modes. A new model quality measure, Modal Coherence Indicator, is introduced. This indicator gives an independent quality tag for each identi ed mode and provides information useful for model validation and order estimation. Two applications from the aircraft and space industry are considered. Both problems are concerned with vibrational analysis of mechanical structures. The rst application is from an extensive experimental vibrational study of the airframe structure of the Saab 2000 commuter aircraft. The second stems from vibrational analysis of a launcher-satellite separation system. In both applications multi-output discrete time state-space models are estimated, which are then used to derive resonant frequencies and damping ratios. New multivariable frequency domain identi cation algorithms are also introduced. Assuming primary data consist of uniformly spaced frequency response measurements, an identi cation algorithm based on realization theory is derived. The algorithm is shown to be robust against bounded noise as well as being consistent. The resulting estimate is shown to be asymptotically normal, and an explicit variance expression is determined. If data originate from an in nite dimensional system, it is shown that the estimated transfer function converges to the transfer function of the truncated balanced realization. Frequency domain subspace based algorithms are also derived and analyzed when the data consist of samples of the Fourier transform of the input and output signals. These algorithms are the frequency domain counterparts of the time domain subspace based algorithms. The frequency domain identi cation methods developed are applied to measured frequency data from a mechanical truss structure which exhibits many lightly damped oscillative modes. With the new methods, high quality state-space models are estimated both in continuous and discrete time. i First of all I would like to express my sincere gratitude to my supervisor, Professor Lennart Ljung. He has made this work possible in many ways, primarily by providing me a place in the group and by o ering me excellent guidance and encouragement during the course of this work. Some of the material in Part II is the result of joint work with Dr. H useyin Ak cay. I thank him for sharing his knowledge with me. Dr. Thomas Abrahamsson, Saab Military Aircraft, and Dr. David Bayard, Jet Propulsion Laboratory, have provided me with experimental data used in this thesis. Thanks! Two persons I am greatly indebted to are Peter Lindskog, who has carefully read the manuscript several times and provided me with numerous valuable comments, and Dr. S oren Andersson, for his excellent comments on Part I. Many thanks also go to Dr. H akan Hjalmarsson, Anders Helmersson, Jonas Sj oberg, H akan Fortell, Dr. Ashok Tikku and Dr. Chun Tung Chou for valuable comments and discussions regarding various parts of this work. I am also grateful to Professor Johan Schouken at Vrije Unvirsiteit in Brussels, and Professor Bart De Moor and Dr. Peter Van Overschee at the Katholieke Universiteit in Leuven for making it possible for me to visit them. The visits were fun and served as a great source of inspiration. I would also like to thank the rest of the people in the group of Automatic Control for their genuine generosity which makes being part of this group so nice. Finally, I would like to thank my wife Maureen, my parents Stig and Sonja and my sister Helen for their in nite support and love. v vi Contents
Smooth Decomposition (SD) is a multivariate data or statistical analysis method to find normal modes and natural frequencies in an spatial data field. The projection used for this method is made such as it keeps the maximum variance possible for the displacement vector and also as it keeps the smoothest motions along time. From this method we can get the "energy" participation in the response of each normal mode during the simulation or the experimental test which can be a relevant information to validate results concerning the identification process. This method of identification can be used for linear and nonlinear systems and uses only output data given that the excitation satisfies some properties normally met by a well chosen random excitation, as a white noise, for example. The objective of this method is to identify systems from their displacement field under ambient excitation which, in many cases, can be hard to compute or to describe. As the method is only based on the covariance matrices of the displacement field and the corresponding velocity field, it is no needed further considerations and approximations. In this point the method is a great tool for modal analysis and system identification. In this paper, the presentation of the method is firstly done which will show us how we can interpret the results of SD for different systems and then the application of SD on simulated multi-DoF damped and undamped systems is performed and discussed to understand how SD can be a great tool for modal analysis. A discussion about the quality of the excitation is also performed.
Volume 2, 2004
Non-stationary systems, which are commonly encountered in many fields of science, are characterized by time-varying features and require time-frequency methods for their analysis. This study considers the problem of identification and model updating of a non-stationary vibrating system. In particular, a number of identification methods and a model updating procedure are evaluated and compared through application to a timevarying "bridge-like" laboratory structure. The identification approaches include Frequency Response Function based parameter estimation techniques, Subspace Identification and Functional Series modelling. All methods are applied to both outputonly and input-output data. Model updating is based upon a theoretical model of the structure obtained using a Rayleigh-Ritz methodology, which is updated to account for time-dependence and nonlinearity via the identification results. Interesting comparisons, among both identification and model updating results, are performed. The results of the study demonstrate high modelling accuracy, illustrating the effectiveness of model updating techniques in non-stationary vibration modelling.
IEEE Transactions on Acoustics, Speech, and Signal Processing
2020
In this paper an extension to the method for the identification of mechanical parameters of nonlinear systems proposed in Breccolotti and Materazzi (2007) for MDoF systems is presented. It can be used for damage identification purposes when damage modifies the linear characteristics of the investigated structure. It is based on the following two main features: the solution of the Fokker-Planck equation that describes the response probabilistic properties of the system when it is excited by external Gaussian loads; and a model updating technique that minimizes the differences between the response of the actual system and that of a parametric system used to identify the unknown parameters. Numerical analysis, that simulate virtual experimental tests, are used in the paper to show the capabilities of the method and to analyse the conditions required for its application.
Proceedings of ISSE'95 - International Symposium on Signals, Systems and Electronics
A new method of non-linear system design, and non-linear subsystem modelling is presented. The method is based on identifying a non-linear model for each subsystem and combining the individual models to solve the entire system. Time domain modelling and simulation is used throughout the procedure but frequency domain characteristics are: also available. Modelling of non-linear subsystem is achieved by
A non-parametric identification technique for the identification ofarbitrary memoryless non-linearities has been presented for a class ofclose-coupled dynamic systems which are commonly met with in mechanical and structural engineering. The method is essentially a regression technique and expresses the non-linearities as series expansions in terms of orthogonal functions. Whereas no limitation on the type of test signals is imposed, the method requires the monitoring of the response of each of the masses in the system. The computational efficiency of the method, its easy implementation on analogue and digital machines and its relative insensitivity to measurement noise make it an attractive approach to the non-parametric identification problem. met with in mechanical and structural systems.?-For instance, a 'cubic spring' type non-linearity would require the determination of third-order kernels whose computation in practice becomes prohibitively expensive.20, In addition, the Wiener approach uses white noise inputs. It is often extremely difficult. if not impossible, to generate large enough inputs of this nature so as to drive large (and often massive) dynamic systems in their non-linear range of response. Applications of such techniques to large non-linear rnultidegree-of-freedom systems are few, if any. This paper presents a relatively simple non-parametric approach to the identification of a class of multidegree-of-freedom (MDF) close-coupled non-linear systems ). The method, following Graupe." is basically a rcgression technique. Masri and Caughey'" were the first to apply this technique to the identification of a single-degree-of-freedom oscillator, by expanding the restoring force in a series of Chebyshev polynomials.22 Herein, we extend the method to include a class of MDF systems, and further generalize it through the use of arbitrary orthogonal sets of functions. The technique has the advantage of being computationally efficient and simple to implement on analogue and digital machines. Unlike the Wiener Kernel approach, it is not restricted to 'white noise' type of inputs, and can be used with almost any type of test input. The choice of the class of models, M, has been governed by its wide usage in problems involving the dynamic response of: (i) full scale building structures, (ii) layered soil ma~ses,'~ (iii) mechanical eq~iprnent.'~. and (iv) machine components and subsystems in, for instance, the nuclear industry.26% " shown that even under extremely noisy measurement conditions, the method yields good results.
Nuclear Engineering and Design, 1979
This paper deals with the identification of complex structural and mechanical systems often encountered in the nuclear industry. Nonparametric identification techniques are used to analyse the response of a class of nonlinear components. Efficient computational algorithms and experimental techniques based on nonparametric system identification methods such as the Wiener-kernel approach and least-squares regression techniques involving the system state-variables are developed and applied to an example system. The variation of system signature with its change in characteristics is studied and the effects of various parameters of the excitation, system, and the computation algorithm on the signature analysis are investigated. The use of the methods for modelling of realistic systems is evaluated and found to be promising. They appear to hold out considerable hope in the damage assessment of critical facilities such as nuclear reactors.
This paper proposes the use of the deterministic-stochastic subspace identification (DSI) method, an input-output parametric linear system identification method, for characterization of nonlinear dynamic structural systems based on their time-varying amplitude-dependent instantaneous (i.e., based on short time-windows) modal parameters. Performance of the DSI method for estimation of instantaneous modal parameters of nonlinear systems is investigated using numerical as well as experimental data. In this study, DSI is used for extracting instantaneous modal parameters of single degree-of-freedom (SDOF) as well as 7-DOF systems with different hysteretic material behavior. Nonlinear responses of the SDOF and 7-DOF systems are simulated due to different seismic excitations using the OpenSees structural analysis software. Modal identification results are compared with those obtained using wavelet transform and the exact values. Effects of four input factors are studied on the variability of identified instantaneous modal parameters: (1) type of material nonlinearity, (2) level of nonlinearity, (3) input excitation, and (4) length of data windows used in the identification. The accuracy of the identified instantaneous modal parameters is evaluated along the response time history while varying the above mentioned input factors. Overall, DSI outperforms the wavelet transform for short-time/instantaneous modal identification of nonlinear structural systems and provides reasonably accurate results especially when the material hysteretic behavior is smooth such as the considered Giuffré -Menegotto-Pinto hysteretic model. Finally, DSI has been applied for short-time modal identification of a full-scale seven-story reinforced concrete shear wall structure based on its measured response to different seismic base excitations on a shake table. The identified instantaneous natural frequencies of the first vibration mode can accurately track the variation in the structure's effective stiffness along its response.
Forced responses are commonly used for system identification, but only initial condition responses may be available when a system has no controllable inputs or forced responses cannot be practically measured. It is therefore meaningful to analyse whether initial condition responses can be used to estimate the system matrix. In this work, the identifiability of the nth order (n41) linear timeinvariant systems from initial condition responses is analysed. Systems with only one measurable state (OMS systems) and those with n measurable states (NMS systems) are considered. Analysis indicates that one initial condition response is not sufficient to uniquely determine the system matrix of an OMS system and n initial condition responses are necessary. The identifiability of an OMS system with data from n independent initial condition responses is equivalent to that of an NMS system with only one initial condition response. Explicit formulations and a non-iterative algorithm are developed for both OMS and NMS systems.
Many systems can be represented as linear time-invariant (LTI) systems in state space with ordinary di erential equations (ODE). Forced responses are often used for model parameter estimation; however, some models are not uniquely identi able from the data of forced responses, or experiments with pure forced response may not be the optimal design. It is thus meaningful to look for other types of data for model parameter estimation through redesigning experiments. In this work, we compare the in uence of forced and initial condition responses on the deterministic identi ability of LTI systems in state space with ODEs as model structure. It is clearly demonstrated that one initial condition vector is equivalent to one column vector of the control matrix for constraining system eigenvectors. The combination of forced and initial condition responses can improve the identi ability of models that are not identi able only from forced responses. Explicit formulations and an algorithm are derived to identify model parameters from the combined data of forced and initial condition responses.
Journal of Zhejiang University Science, 2004
System identification is a method for using measured data to create or improve a mathematical model of the object being tested. From the measured data however, noise is noticed at the beginning of the response. One solution to avoid this noise problem is to skip the noisy data and then use the initial conditions as active parameters, to be found by using the system identification process. This paper describes the development of the equations for setting up the initial conditions as active parameters. The simulated data and response data from actual shear buildings were used to prove the accuracy of both the algorithm and the computer program, which include the initial conditions as active parameters. The numerical and experimental model analysis showed that the value of mass, stiffness and frequency were very reasonable and that the computed acceleration and measured acceleration matched very well.
Journal of The Franklin Institute-engineering and Applied Mathematics, 1985
This paper considers the problem of identifying the parameters and initial conditions of systems described by linear differential equations with time-varying coefficients. A new approach is proposed which is based on the idea of using orthogonal functions to represent the input—output data, as well as the unknown time-varying parameters of the system. Using certain properties of the orthogonal functions, an algorithm is constructed which reduces the identification problem to that of solving a linear algebraic system of equations.
Mechanical Systems and Signal Processing, 2009
The problem of parametric output-only identification of a time-varying structure based on vector random vibration signal measurements is considered. A Functional Series Vector Time-dependent AutoRegressive Moving Average (FS-VTARMA) method is introduced and employed for the identification of a "bridge-like" laboratory structure consisting of a beam and a moving mass. The identification is based on three simultaneously measured vibration response signals obtained during a single experiment. The method is judged against baseline modelling based on multiple "frozen-configuration" stationary experiments, and is shown to be effective and capable of accurately tracking the dynamics. Additional comparisons with a recursive Pseudo-Linear Regression VTARMA (PLR-VTARMA) method and a Short Time Canonical Variate Analysis (ST-CVA) subspace method are made and demonstrate the method's superior achievable accuracy and model parsimony.
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