Paper
19 April 2007 Quantification of damage detection schemes using receiver operating characteristic (ROC) curves
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Abstract
In this work we detect damage in a composite to metal bolted joint subject to ambient vibrations and strong temperature fluctuations. Damage to the joint is considered to be a degradation of the connection strength implemented by loosening the bolts. The system is excited with a signal that conforms to the Pierson-Moskowitz distribution for wave height and represents a possible loading this component would be subject to in situ. We show that as the bolts are loosened, increasing amounts of nonlinearity are introduced in the form of impact discontinuities and stick-slip behavior. The presence of the nonlinearity, hence the damage, is detected by drawing comparisons between the response data and surrogate data conforming to the null hypothesis of an undamaged, linear system. Two metrics are used for comparison purposes: nonlinear prediction error and the bicoherence. Results are displayed using Receiver Operating Characteristic (ROC) curves. The ROC curve quantifies the trade-off between false positives (type I errors) and false negatives (type II errors). Type I errors can be expressed as the probability of false alarm and 1 - type II error is the probability of detection. We demonstrate that ROC curves provide a unified quantifiable approach for directly comparing the merits of different detection schemes.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen Trickey, Mark Seaver, and Jon Nichols "Quantification of damage detection schemes using receiver operating characteristic (ROC) curves", Proc. SPIE 6531, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2007, 653107 (19 April 2007); https://doi.org/10.1117/12.715210
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Cited by 1 scholarly publication.
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KEYWORDS
Composites

Damage detection

Receivers

Fourier transforms

Data modeling

Sensors

Fiber optics

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