&lt;title&gt;Structural integrity monitoring of composite patch repairs using wavelet analysis and neural networks&lt;/title&gt;

Amaravadi, Venkata Kasi; Mitchell, Kyle; Rao, V. Sree Hari; Derriso, Mark M.

doi:10.1117/12.474655

Cited by 12 publications

(8 citation statements)

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“…For instance, most current vibration-based damage detection techniques observe changes in natural frequencies, modes of vibration, and frequency response functions (Loh and Tou, 1995;Doebling et al, 1996;Smyth et al, 2000;Farrar et al, 2001). Other techniques use subspace identification and updating (Abdalla et al, 1998;Pappa et al, 1998;Zimmerman, 2000;D'Souza and Epureanu, 2005a, b), wavelet analyses (Sohn and Farrar, 2001;Amizic et al, 2002;Amaravadi et al, 2002), and Ritz vectors (Cao and Zimmerman, 1999;Zimmerman, 1999). All these methods have been well developed for monitoring linear structures.…”

Section: Introductionmentioning

confidence: 97%

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Yin

Epureanu

2005

Journal of Fluids and Structures

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Section: Introductionmentioning

confidence: 97%

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Yin

Epureanu

2005

Journal of Fluids and Structures

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“…they are focused on determining changes in the frequencies and modes of vibration [14,24,36,49,60]. Other similar techniques use wavelet analyses [3][4][5]62], Ritz vectors [11,77], stochastic approaches [57,64], subspace updating [1,26,32,50,51,72,78,79], or evolutionary algorithms [80]. All these methods have been developed for monitoring linear structures.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Nonlinear Dynamics for Accurate Identification of Stiffness Loss in a Thermo-Shielding Panel

2005

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The dynamics of a panel forced by transverse loads and undergoing limit cycle oscillations and chaos is investigated. The nonlinear von Karman plate theory is used to obtain a model for healthy and damaged panels. Damage is modeled by a loss of stiffness in a portion of the plate. The presence of low levels of damage is identified by using an external nonlinear excitation and analyzing the attractor of the resulting dynamics in state space. Most of the current studies of such problems are based on linear theories and linear structures. In contrast, the results presented are obtained by using and enhancing nonlinear and chaotic dynamics, and have the advantage of an increased accuracy in detecting damage and monitoring structural health.

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“…In the first category, modal analysis and frequency based approaches [1,11,24] are common. Other approaches have utilized different modeling techniques to approximate system behavior such as neural networks or wavelets [2,3]. While successful for linear systems, these methods suffer when applied to highly nonlinear and chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Resonance and Attractor Morphing Quantified by Sensitivity Vector Fields for Parameter Reconstruction

Hashmi

Epureanu

2006

Nonlinear Dyn

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A novel method of parameter variation reconstruction for systems exhibiting chaotic dynamics is presented. The algorithm reconstructs variations of system parameters without the need for explicit system equations of motion, or knowledge of the nominal parameter values. The concept of a sensitivity vector field (SVF) is developed. This construct captures geometrical deformations to the dynamical attractor of the system in state space. These fields are collected by means of a proposed unique approach referred to as point cloud averaging (PCA). PCA is applied to discrete time series data from the system with nominal parameter values (healthy) and the system with changed parameters. Test variations are reconstructed from an optimal basis of SVF snapshots which is generated by means of proper orthogonal decomposition. The method is applied to two system models, a magneto-elastic oscillator (MEO) and an atomic force microscope (AFM). The method is shown to be highly accurate, and capable of identifying multiple simultaneous variations. The success of the method as applied to an AFM and a MEO indicates a potential for highly accurate readings by exploiting the geometric features of observed chaotic vibrations. An exciting new phenomenon referred to as sensitivity resonance was also observed, and some implications regarding its use in further improving algorithm performance are discussed.

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<title>Structural integrity monitoring of composite patch repairs using wavelet analysis and neural networks</title>

Cited by 12 publications

References 0 publications

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Enhanced Nonlinear Dynamics for Accurate Identification of Stiffness Loss in a Thermo-Shielding Panel

Sensitivity Resonance and Attractor Morphing Quantified by Sensitivity Vector Fields for Parameter Reconstruction

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