Open AccessA Bayesian Approach to Modal Acoustic Emission Source Location
Author(s) -
Boris A. Zárate
Publication year - 2019
Publication title -
eco matematico/eco matemático
Language(s) - English
Resource type - Journals
eISSN - 2462-8794
pISSN - 1794-8231
DOI - 10.22463/17948231.2536
Subject(s) - acoustic emission , morlet wavelet , markov chain monte carlo , waveform , acoustics , bayesian inference , bayesian probability , computer science , algorithm , wavelet , wavelet transform , physics , telecommunications , artificial intelligence , discrete wavelet transform , radar
Modal Acoustic Emission (MAE) is a branch of Acoustic Emission (AE) with proven capabilities for Structural Health Monitoring (SHM) of plate-like structures. MAE differences from AE in that MAE uses the understanding of the wave propagation to characterize and locate the source. The analysis of the waveform includes the use of time frequency techniques to determine the Time Of Arrival (TOA) of the different modes. This paper proposes the use of Bayesian inference to quantify the uncertainty in the source location for two different MAE location techniques. The first technique uses only the TOA of the extensional (symmetric) mode, while the second technique uses the TOA of both extensional and flexural (antisymmetric) modes. The Morlet wavelet is used to determine the scalogram of the waveform. The scalogram is reassigned and Markov Chain Monte Carlo (MCMC) is used to sample the posterior distribution built through Bayesian inference. Results are presented from location of Pencil Lead Breaks (PLBs) in an aluminum plate of 1/8in of thickness and 36in by 36in. Results show that using the TOA of only the symmetric mode leads to a lower level of uncertainty compared to using both extensional and flexural modes, because of the difficulty in assessing the time of arrival of the flexural mode.