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Inverse Sensitivity Methods In Linear Structural Damage Detection Using Vibration DataVenkatesha, S 03 1900 (has links)
The thesis addresses the problem of structural damage detection using inverse sensitivity based methods. The focus here is on characterization with regard to identification, location, and, quantification of structural damage in linear time invariant (LTI) systems, using vibration data. The study encompasses both analytical and experimental methods. A suite of five algorithms for damage detection, namely, inverse eigensensitivity method that is refined to account for cross orthogonality between distinct modes, damping dependent eigensolutions, and sensitivity with respect to points of antiresonance and minima, inverse FRF method that includes refinements in terms of inclusion of second order sensitivity, response function method (RFM) based on first order Taylor’s expansion, a newly proposed inverse sensitivity method based on singular values of FRF matrix, and method based on response time histories, are presented. The scope of these methods visàvis the need for model reduction, ability to deal with incomplete data, illposedness of governing equations and the need for regularization, sensitivity with respect to measurement noise, ability to identify damping characteristics, the highest and lowest magnitudes of changes in structural properties, and the ability to characterize systems with closely spaced natural frequencies that the methods can detect are discussed. The performance of proposed procedures is illustrated by considering a five degreesoffreedom (dof) massspringdashpot system and subsequently applied on three archetypal structural systems using analytical and experimental methods. In the examples presented, factors, such as, completeness of measured data in time and frequency, nature (proportional/nonproportional) and magnitude of damping, levels of changes in structural properties, modal truncations, number of governing equations for system parameters, and efficacy of regularization techniques are investigated. The study also highlights the difficulties in implementing the damage detection algorithm based on real life noisy vibration data. A comparative study on the suitability of each of these methods in locating and quantifying of different damage scenarios has been reported. A critical review of performance of the various methods is presented. The thesis concludes with a summary on the contributions made and also deliberates on future avenues for research and development in this area of research.

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ParameterDependent Lyapunov Functions and Stability Analysis of Linear ParameterDependent Dynamical SystemsZhang, Xiping 27 October 2003 (has links)
The purpose of this thesis is to develop new stability conditions for several linear dynamic systems, including linear parametervarying (LPV), timedelay systems (LPVTD), slow LPV
systems, and parameterdependent linear time invariant (LTI) systems. These stability conditions are less conservative and/or computationally easier to apply than existing ones.
This dissertation is composed of four parts. In the first part of this thesis, the complete stability domain for LTI parameterdependent (LTIPD) systems is synthesized by extending existing results in the literature. This domain is calculated through a guardian map which involves the determinant of the Kronecker sum of a matrix with itself. The stability domain is
synthesized for both single and multiparameter dependent LTI systems. The singleparameter case is easily computable, whereas the multiparameter case is more involved. The determinant of the
bialternate sum of a matrix with itself is also exploited to reduce the computational complexity.
In the second part of the thesis, a class of parameterdependent Lyapunov functions is proposed, which can be used to assess the stability properties of singleparameter LTIPD systems in a nonconservative manner. It is shown
that stability of LTIPD systems is equivalent to the existence of a Lyapunov function of a polynomial type (in terms of the parameter) of known, bounded degree satisfying two matrix inequalities. The bound of polynomial degree of the Lyapunov functions is then reduced by taking advantage of the fact that the Lyapunov matrices are symmetric. If the matrix multiplying the parameter is not full rank, the polynomial order
can be reduced even further. It is also shown that checking the feasibility of these matrix
inequalities over a compact set can be cast as a convex optimization problem. Such Lyapunov functions and stability conditions for affine singleparameter LTIPD systems are then generalized to singleparameter polynomiallydependent LTIPD systems and affine multiparameter LTIPD systems.
The third part of the thesis provides one of the first attempts to derive computationally tractable criteria for analyzing the stability of LPV timedelayed systems. It presents both
delayindependent and delaydependent stability conditions, which are derived using appropriately selected LyapunovKrasovskii functionals. According to the system parameter dependence, these functionals can be selected to obtain increasingly nonconservative results. Gridding techniques may be used to cast these tests as Linear Matrix Inequalities (LMI's). In cases when
the system matrices depend affinely or quadratically on the parameter, gridding may be avoided. These LMI's can be solved efficiently using available software. A numerical example of a
timedelayed system motivated by a metal removal process is used to demonstrate the theoretical results.
In the last part of the thesis, topics for future
investigation are proposed. Among the most interesting avenues for research in this context, it is proposed to extend the existing stability analysis results to controller synthesis, which will be based on the same Lyapunov functions used
to derive the nonconservative stability conditions. While designing the dynamic ontroller for linear and parameterdependent systems, it is desired to take the advantage of the rank deficiency of the system matrix multiplying the parameter such that the controller is of lower dimension, or rank deficient without sacrificing the performance of closedloop systems.

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