A novel Bayesian approach to acoustic emission data analysis
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Updated: Mar 16, 2026

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Published on: May 9, 2017
E Agletdinov1, E Pomponi2, D Merson1
1Institute of Advanced Technologies, Togliatti State University, 445667, Russia.
Abstract:
Acoustic emission (AE) technique is a popular tool for materials characterization and non-destructive testing. Originating from the stochastic motion of defects in solids, AE is a random process by nature. The challenging problem arises whenever an attempt is made to identify specific points corresponding to the changes in the trends in the fluctuating AE time series. A general Bayesian framework is proposed for the analysis of AE time series, aiming at automated finding the breakpoints signaling a crossover in the dynamics of underlying AE sources.
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