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Bayesian estimation of acoustic emissions source in plate structures using particle‐based stochastic filtering
Author(s) -
Sen Debarshi,
Erazo Kalil,
Nagarajaiah Satish
Publication year - 2017
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2005
Subject(s) - particle filter , posterior probability , algorithm , kalman filter , filter (signal processing) , mathematics , bayesian probability , computer science , statistics , computer vision
Summary The application of particle‐based stochastic filters to acoustic emission source localization in plate structures is presented. The approach employs time‐of‐flight measurements of guided waves using triangulation to estimate the acoustic emission source coordinates in a probabilistic framework using Bayesian inference, incorporating uncertainties related to material properties, measurement noise, and geometry of the system of interest. The estimate of the source location is given by a probability density function conditional on the guided wave measurements, found using particle‐based stochastic simulation algorithms; in this setting, a set of particles is used to explore the space of possible source locations and efficiently estimate the posterior. The use of 2 filters is explored: the ensemble Kalman filter and the particle filter. The former filter assumes that the posterior distribution can be approximated by a Gaussian distribution, although the latter provides a nonparametric estimate of the posterior in the form of a weighted set of samples, overcoming the challenges related to the evaluation of high‐dimensional integrals in an efficient way. Results of an experimental validation study conducted in a laboratory environment demonstrate the accuracy and efficiency of the particle filter‐based approach. In particular, it is shown that the proposed particle filter‐based approach has the capability to locate the emission source under minimal instrumentation, providing confidence intervals as a quantitative measure of the uncertainty in the estimates.