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Proceedings Paper

Performance verification of a bivariate regressive adaptive index for structural health monitoring
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Paper Abstract

This study focuses on data-driven methods for structural health monitoring and introduces a Bivariate Regressive Adaptive INdex (BRAIN) for damage detection in a decentralized, wireless sensor network. BRAIN utilizes a dynamic damage sensitive feature (DSF) that automatically adapts to the data set, extracting the most damage sensitive model features, which vary with location, damage severity, loading condition and model type. This data-driven feature is key to providing the most flexible damage sensitive feature incorporating all available data for a given application to enhance reliability by including heterogeneous sensor arrays. This study will first evaluate several regressive-type models used for time-series damage detection, including common homogeneous formats and newly proposed heterogeneous descriptors and then demonstrate the performance of the newly proposed dynamic DSF against a comparable static DSF. Performance will be validated by documenting their damage success rates on repeated simulations of randomly-excited thin beams with minor levels of damage. It will be shown that BRAIN dramatically increases the detection capabilities over static, homogeneous damage detection frameworks.

Paper Details

Date Published: 10 April 2007
PDF: 10 pages
Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65291Q (10 April 2007); doi: 10.1117/12.715690
Show Author Affiliations
Su Su, Univ. of Notre Dame (United States)
Tracy Kijewski-Correa, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 6529:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007
Masayoshi Tomizuka; Chung-Bang Yun; Victor Giurgiutiu, Editor(s)

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