Adaptive measurement selection for progressive damage estimation

Wenfan Zhou; Narayan Kovvali; Antonia Papandreou-Suppappola; Aditi Chattopadhyay; Pedro Peralta

doi:10.1117/12.882029

Adaptive measurement selection for progressive damage estimation

Wenfan Zhou, Narayan Kovvali, Antonia Papandreou-Suppappola, Aditi Chattopadhyay, Pedro Peralta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
DOIs	https://doi.org/10.1117/12.882029
State	Published - May 26 2011
Event	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011 - San Diego, CA, United States Duration: Mar 7 2011 → Mar 10 2011

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7981
ISSN (Print)	0277-786X

Other

Other	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
Country/Territory	United States
City	San Diego, CA
Period	3/7/11 → 3/10/11

Keywords

adaptive measurement selection
noise suppression
progressive damage diagnosis
sequential Monte Carlo
structural health monitoring

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.882029

Cite this

Zhou, W., Kovvali, N., Papandreou-Suppappola, A., Chattopadhyay, A., & Peralta, P. (2011). Adaptive measurement selection for progressive damage estimation. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011 Article 798127 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7981). https://doi.org/10.1117/12.882029

Adaptive measurement selection for progressive damage estimation. / Zhou, Wenfan; Kovvali, Narayan; Papandreou-Suppappola, Antonia et al.
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011. 2011. 798127 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7981).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhou, W, Kovvali, N, Papandreou-Suppappola, A , Chattopadhyay, A & Peralta, P 2011, Adaptive measurement selection for progressive damage estimation. in Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011., 798127, Proceedings of SPIE - The International Society for Optical Engineering, vol. 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, San Diego, CA, United States, 3/7/11. https://doi.org/10.1117/12.882029

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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.",

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AB - 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.

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Adaptive measurement selection for progressive damage estimation

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Keywords

ASJC Scopus subject areas

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