Abstract
ABSTRACT This paper proposes an adaptive Unscented Kalman Filter (UKF) algorithm for Acoustic Emission (AE) source localization in plate-like structures in noisy environments. Over all, 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 instrument ed 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. Keywords: Acoustic Emission (AE), Adaptive Unscented Kalman Filter (UKF), Binary Hypothesis Test, Lamb waves, inflight structural health monitoring. 1. INTRODUCTION The continual aging of aircraft fleets resulted in new safety requirements and necessitated upgrading the exciting maintenance technologies
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