18 April 2011 Statistical quantification of the uncertainty in transmissibility feature for structural condition binary classification
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Abstract
Transmissibility-related features are one class of indicators used to detect structural defects, especially because of their sensitivity to local changes. In this paper, we consider a SIMO identification model and regard the change in transmissibility as a feature indicating damage occurrence. Both inherent randomness in the system identification process and the noise contamination (or other types of measurement/sampling/quantization variabilities) are included as the sources of transmissibility uncertainty. The uncertainty quantification is necessary to group-classify the measurements into either undamaged or damaged (binary) conditions with a better understanding of Type I/II trade-offs. A sensitivity research is deployed in this paper, where Receiver Operating Characteristic (ROC) curves for individual frequency lines are given with different damage levels and extraneous noise levels, and Area Under Curve (AUC) will be evaluated as the key performance metric across the entire frequency domain. The paper concludes that regions near resonance will have the best hypothesis test performance in terms of sensitivity and specificity.
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Zhu Mao, Zhu Mao, Michael Todd, Michael Todd, } "Statistical quantification of the uncertainty in transmissibility feature for structural condition binary classification", Proc. SPIE 7984, Health Monitoring of Structural and Biological Systems 2011, 79842H (18 April 2011); doi: 10.1117/12.879914; https://doi.org/10.1117/12.879914
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