Bayesian estimation of acoustic emissions source in plate structures using particle-based stochastic filtering

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.