Application of frequency domain ARX models and extreme value statistics to damage detection

Timothy R. Fasel; Hoon Sohn; Charles R. Farrar

doi:10.1117/12.482715

18 August 2003 Application of frequency domain ARX models and extreme value statistics to damage detection

Timothy R. Fasel, Hoon Sohn, Charles R. Farrar

Proceedings Volume 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures; (2003) https://doi.org/10.1117/12.482715
Event: Smart Structures and Materials, 2003, San Diego, California, United States

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.

Citation Download Citation

Timothy R. Fasel, Hoon Sohn, and Charles R. Farrar "Application of frequency domain ARX models and extreme value statistics to damage detection", Proc. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, (18 August 2003); https://doi.org/10.1117/12.482715

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