Probabilistic uncertainty quantification of wavelet-transform-based structural health monitoring features

Aral Sarrafi; Zhu Mao

doi:10.1117/12.2222048

1 April 2016 Probabilistic uncertainty quantification of wavelet-transform-based structural health monitoring features

Aral Sarrafi, Zhu Mao

Proceedings Volume 9805, Health Monitoring of Structural and Biological Systems 2016; 98051N (2016) https://doi.org/10.1117/12.2222048
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2016, Las Vegas, Nevada, United States

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.

Citation Download Citation

Aral Sarrafi and Zhu Mao "Probabilistic uncertainty quantification of wavelet-transform-based structural health monitoring features", Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98051N (1 April 2016); https://doi.org/10.1117/12.2222048

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available