A Bayesian Approach for Localization of Acoustic Emission Source in Plate-Like Structures

Abstract

This paper presents a Bayesian approach for localizing acoustic emission (AE) source in plate-like structures with consideration of uncertainties from modeling error and measurement noise. A PZT sensor network is deployed to monitor and acquire AE wave signals released by possible damage. By using continuous wavelet transform (CWT), the time-of-flight (TOF) information of the AE wave signals is extracted and measured. With a theoretical TOF model, a Bayesian parameter identification procedure is developed to obtain the AE source location and the wave velocity at a specific frequency simultaneously and meanwhile quantify their uncertainties. It is based on Bayes’ theorem that the posterior distributions of the parameters about the AE source location and the wave velocity are obtained by relating their priors and the likelihood of the measured time difference data. A Markov chain Monte Carlo (MCMC) algorithm is employed to draw samples to approximate the posteriors. Also, a data fusion scheme is performed to fuse results identified at multiple frequencies to increase accuracy and reduce uncertainty of the final localization results. Experimental studies on a stiffened aluminum panel with simulated AE events by pensile lead breaks (PLBs) are conducted to validate the proposed Bayesian AE source localization approach.

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

“…The probability distribution for the elastic modulus of the granite blocks is updated by integrating the information from prior knowledge, the data from sonic test, the data from ultrasonic test, as well as the data from compressive strength test. In Yan and Tang, 49 the acoustic emission source in plate-like structures is localized based on a Bayesian approach. Multiple 2D Gaussian distributions obtained from time difference data at different frequencies are merged to provide the final probability distribution of the acoustic emission source location.…”

Performance Investigation of Two AE Source Location Techniques on a Planar Multilayer Structure

“…18 Several recent methods like Bayesian approach and Dong's velocity-free localization approach take the velocity of the wave as an additional unknown parameter and solve it with a source coordinate. 19,20 The collaborative localization method that uses analytical and iterative solutions for microseismic/acoustic emission sources, can filter the abnormal arrivals and improve the accuracy of the source location as well. 21 Nevertheless, the difference of arrival time (DOAT) method, similar to the Delta T method as described by Baxter et al, records arrival time differences from several locations, for op-timizing source location of the complex, multilayered structure.…”

An Optimal Image-Based Method for Identification of Acoustic Emission (AE) Sources in Plate-Like Structures Using a Lead Zirconium Titanate (PZT) Sensor Array

“…In addition, probabilistic and statistical approaches, which can characterize the AE source with consideration of various uncertainties, in particular uncertainties in extracted time information and wave velocities, have received more and more attention in AE source identification. These approaches includes extended Kalman filter (EKF) [ 18 ], unscented Kalman filter (UKF) [ 15 ], particle filter (PF) [ 19 ] and Bayesian methods which are realized by Monte Carlo simulations [ 20 , 21 , 22 ]. In addition, machine learning approaches, such as artificial neural network (ANN) and support vector machine (SVM), have also been employed for AE source localization [ 23 , 24 , 25 , 26 ].…”