A novel Bayesian approach to acoustic emission data analysis

E Agletdinov; E Pomponi; D Merson; A Vinogradov

doi:10.1016/j.ultras.2016.07.014

A novel Bayesian approach to acoustic emission data analysis

Ultrasonics. 2016 Dec:72:89-94. doi: 10.1016/j.ultras.2016.07.014. Epub 2016 Jul 25.

Authors

E Agletdinov¹, E Pomponi², D Merson¹, A Vinogradov³

Affiliations

¹ Institute of Advanced Technologies, Togliatti State University, 445667, Russia.
² Department of Data Science and Analytic, CGnal s.p.a., Via Carducci 38, 20122 Milano, Italy.
³ Institute of Advanced Technologies, Togliatti State University, 445667, Russia; Department of Engineering Design and Materials, Norwegian University of Science and Technology - NTNU, Trondheim 7491, Norway. Electronic address: alexei.vinogradov@ntnu.no.

PMID: 27494093
DOI: 10.1016/j.ultras.2016.07.014

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.

Keywords: Acoustic emission; Bayesian probability; Random time-series; Signal processing.

Publication types

Research Support, Non-U.S. Gov't