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Proceedings Paper

Adaptive measurement selection for progressive damage estimation
Author(s): Wenfan Zhou; Narayan Kovvali; Antonia Papandreou-Suppappola; Aditi Chattopadhyay; Pedro Peralta
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Paper Abstract

Noise and interference in sensor measurements degrade the quality of data and have a negative impact on the performance of structural damage diagnosis systems. In this paper, a novel adaptive measurement screening approach is presented to automatically select the most informative measurements and use them intelligently for structural damage estimation. The method is implemented efficiently in a sequential Monte Carlo (SMC) setting using particle filtering. The noise suppression and improved damage estimation capability of the proposed method is demonstrated by an application to the problem of estimating progressive fatigue damage in an aluminum compact-tension (CT) sample using noisy PZT sensor measurements.

Paper Details

Date Published: 25 April 2011
PDF: 8 pages
Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798127 (25 April 2011); doi: 10.1117/12.882029
Show Author Affiliations
Wenfan Zhou, Arizona State Univ. (United States)
Narayan Kovvali, Arizona State Univ. (United States)
Antonia Papandreou-Suppappola, Arizona State Univ. (United States)
Aditi Chattopadhyay, Arizona State Univ. (United States)
Pedro Peralta, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 7981:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
Masayoshi Tomizuka, Editor(s)

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