Adaptive measurement selection for progressive damage estimation

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

doi:10.1117/12.882029

25 April 2011 Adaptive measurement selection for progressive damage estimation

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

Proceedings Volume 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011; 798127 (2011) https://doi.org/10.1117/12.882029
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2011, San Diego, California, United States

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.

Citation Download Citation

Wenfan Zhou, Narayan Kovvali, Antonia Papandreou-Suppappola, Aditi Chattopadhyay, and Pedro Peralta "Adaptive measurement selection for progressive damage estimation", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 798127 (25 April 2011); https://doi.org/10.1117/12.882029

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