Share Email Print
cover

Proceedings Paper

Application of frequency domain ARX models and extreme value statistics to damage detection
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.

Paper Details

Date Published: 18 August 2003
PDF: 12 pages
Proc. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, (18 August 2003); doi: 10.1117/12.482715
Show Author Affiliations
Timothy R. Fasel, Los Alamos National Lab. (United States)
Hoon Sohn, Los Alamos National Lab. (United States)
Charles R. Farrar, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 5057:
Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures
Shih-Chi Liu, Editor(s)

© SPIE. Terms of Use
Back to Top