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Proceedings Paper

Probabilistic uncertainty quantification of wavelet-transform-based structural health monitoring features
Author(s): Aral Sarrafi; Zhu Mao
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Paper Abstract

In the application of Structural Health Monitoring (SHM), processing the online-acquired data plays a very important role, among which wavelet transform is an outstanding tool and compared to Fourier transform, it handles the nonstationary behaviors in the time series in an adaptive fashion. When dealing with time-variant data, there are uncertainties from numerous resources inherent to the feature estimation, such as measurement noise, operational and environmental variability, hardware limitation, etc. The corruption from uncertainty will make the data interpretation ambiguous and thereby dramatically degrades the decision quality with regard to the occurrence, location, severity, and extent of damages. This paper derives a probabilistic model to quantify analytically the uncertainty of wavelet transform feature as a random variable, and variance is derived analytically in this work. Considering central limit theorem, Gaussian probability density function characterizes the distribution and this has been validated via Monte Carlo testing. By fully characterizing the uncertainty, the damage detection implementations may be facilitated with a quantified false alarm rate and miss catch rate.

Paper Details

Date Published: 1 April 2016
PDF: 10 pages
Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98051N (1 April 2016); doi: 10.1117/12.2222048
Show Author Affiliations
Aral Sarrafi, Univ. of Massachusetts Lowell (United States)
Zhu Mao, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 9805:
Health Monitoring of Structural and Biological Systems 2016
Tribikram Kundu, Editor(s)

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