Paper
15 July 2002 Structural integrity monitoring of composite patch repairs using wavelet analysis and neural networks
Venkata Kasi Amaravadi, Kyle Mitchell, Vittal S. Rao, Mark M. Derriso
Author Affiliations +
Abstract
Bonded composite repairs for the reinforcement of damaged aircraft structures are effective in extending the life of aging airframes. The structural integrity of the composite patch repair in terms of disbond, fracture at the bond-lines, delamination, and structural crack growth is to be investigated before the composite repair technology can be adopted by the aerospace industry. We have developed structural health monitoring techniques for locating, identifying, and quantifying damages using the changes in the dynamical response of the repaired structure. A signal-based health monitoring algorithms wavelet transforms, have been developed for monitoring the structural integrity of composite patches, which detects variations induced by small changes in the vibration signature of the repaired structure. In this paper, threshold wavelet maps and neural networks have been integrated to detect and quantify the damage (s) in the composite patch repairs. Neural networks are utilized to find the extent of the damage. This method is also capable of detecting multiple damages. The mode shapes are obtained analytically using finite element analysis and experimentally with laser vibrometer. We have also developed a wireless data acquisition system for collection, feature extraction, and transmission of vibration data. The results of the damage location and extent estimation in the composite patch repairs are satisfactory.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Venkata Kasi Amaravadi, Kyle Mitchell, Vittal S. Rao, and Mark M. Derriso "Structural integrity monitoring of composite patch repairs using wavelet analysis and neural networks", Proc. SPIE 4701, Smart Structures and Materials 2002: Smart Structures and Integrated Systems, (15 July 2002); https://doi.org/10.1117/12.474655
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Cited by 13 scholarly publications.
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KEYWORDS
Wavelets

Composites

Neural networks

Aluminum

Adhesives

Finite element methods

Algorithm development

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