Paper
20 April 2000 Combining damage detection methods to improve probability of detection
Gabriel V. Garcia, Roberto A. Osegueda
Author Affiliations +
Abstract
The objective of this work is to improve the probability of detecting damage and reduce the probability of false positives by combining damage detection methods. Most vibration-based NDD methods are derived from expressions relating the modal properties (i.e., mode shapes and frequencies) and/or the physical properties (i.e., stiffness or flexibility) of an undamaged structure to the modal properties of a damaged structure. These methods utilize some form of a damage indicator to identify the existence and location of damage in a structure. The basic assumption is that the modal and physical properties of the undamaged and damaged structure will differ. Thus, by measuring and comparing the modal properties of the damaged and undamaged structure one can infer whether or not damage exists and in some cases the location of the damage. In this work, we present a methodology to combine the results of the different NDD methods using the techniques of pattern recognition. To accomplish this task, we begin with a review of pattern recognition. Next, we develop a methodology to combine the results of the different NDD methods. To investigate the applicability of the combined approach we perform damage detection on a beam using two damage detection methods (Damage Index Method and a method that utilizes the parameters of an ARMA model as damage indicators) separately and then combined using the developed methodology. Finally, a comparison of the probability of detection and the probability of a false positive is made between the combined approach and the NDD methods applied separately.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriel V. Garcia and Roberto A. Osegueda "Combining damage detection methods to improve probability of detection", Proc. SPIE 3988, Smart Structures and Materials 2000: Smart Systems for Bridges, Structures, and Highways, (20 April 2000); https://doi.org/10.1117/12.383134
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Damage detection

Pattern recognition

Image classification

Transducers

Aluminum

Autoregressive models

Analytical research

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