17 April 2013 Adaptive unscented Kalman filter (UKF) for acoustic emission (AE) source localization in noisy environment
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Abstract
This paper proposes an adaptive Unscented Kalman Filter (UKF) algorithm for Acoustic Emission (AE) source localization in plate-like structures in noisy environments. Overall, the proposed approach consists of four main stages: 1) feature extraction, 2) sensor selection based on a binary hypothesis testing, 3) sensor weighting based on a well-defined weighting function, and 4) estimation of the AE source based on the UKF. The performance of the proposed algorithm is validated through pencil lead breaks performed on an aluminum plate instrumented with a sparse array of piezoelectric sensors. To simulate highly noisy environment, two piezoelectric transducers have been used to continually generating high power white noise during testing.
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Ehsan Dehghan Niri, Ehsan Dehghan Niri, Alireza Farhidzadeh, Alireza Farhidzadeh, Salvatore Salamone, Salvatore Salamone, } "Adaptive unscented Kalman filter (UKF) for acoustic emission (AE) source localization in noisy environment", Proc. SPIE 8695, Health Monitoring of Structural and Biological Systems 2013, 869518 (17 April 2013); doi: 10.1117/12.2008617; https://doi.org/10.1117/12.2008617
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