Defect classification in sparsity-based structural health monitoring

Andrew Golato; Fauzia Ahmad; Sridhar Santhanam; Moeness G. Amin

doi:10.1117/12.2262925

5 May 2017 Defect classification in sparsity-based structural health monitoring

Andrew Golato, Fauzia Ahmad, Sridhar Santhanam, Moeness G. Amin

Proceedings Volume 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics; 102110A (2017) https://doi.org/10.1117/12.2262925
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Guided waves have gained popularity in structural health monitoring (SHM) due to their ability to inspect large areas with little attenuation, while providing rich interactions with defects. For thin-walled structures, the propagating waves are Lamb waves, which are a complex but well understood type of guided waves. Recent works have cast the defect localization problem of Lamb wave based SHM within the sparse reconstruction framework. These methods make use of a linear model relating the measurements with the scene reflectivity under the assumption of point-like defects. However, most structural defects are not perfect points but tend to assume specific forms, such as surface cracks or internal cracks. Knowledge of the "type" of defects is useful in the assessment phase of SHM. In this paper, we present a dual purpose sparsity-based imaging scheme which, in addition to accurately localizing defects, properly classifies the defects present simultaneously. The proposed approach takes advantage of the bias exhibited by certain types of defects toward a specific Lamb wave mode. For example, some defects strongly interact with the anti-symmetric modes, while others strongly interact with the symmetric modes. We build model based dictionaries for the fundamental symmetric and anti-symmetric wave modes, which are then utilized in unison to properly localize and classify the defects present. Simulated data of surface and internal defects in a thin Aluminum plate are used to validate the proposed scheme.

Citation Download Citation

Andrew Golato, Fauzia Ahmad, Sridhar Santhanam, and Moeness G. Amin "Defect classification in sparsity-based structural health monitoring", Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110A (5 May 2017); https://doi.org/10.1117/12.2262925

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Structural health monitoring

Transducers

Wave propagation

Associative arrays

Reflectivity

Waveguides

Wave plates

Show All Keywords

Keywords/Phrases

Search In:

Publication Years