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.
Highlights
The increasing emphasis on integrity of critical structures such as aircrafts urges the needs to monitor structures in situ and real-time to detect damages at an early stage to prevent catastrophic failure
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
With the development of modal acoustic emission (MAE) technique in which the concept of guided waves is introduced to improve the interpretation of the AE wave signals, the continuous wavelet transform (CWT) has become a useful tool for the time-frequency representation of transient AE waves in dispersive medium with its advantage of good resolution in both time and frequency domains
Summary
The increasing emphasis on integrity of critical structures such as aircrafts urges the needs to monitor structures in situ and real-time to detect damages at an early stage to prevent catastrophic failure. With the known positions of the sensors and the velocities of the AE wave signals, the location of the AE source can be determined by solving a set of nonlinear equations directly or by iterative optimization algorithms These triangulation approaches are originally developed for isotropic structures and extended to anisotropic composite structures, for which damage caused by low velocity impact is a major concern [13, 14]. With the development of modal acoustic emission (MAE) technique in which the concept of guided waves is introduced to improve the interpretation of the AE wave signals, the continuous wavelet transform (CWT) has become a useful tool for the time-frequency representation of transient AE waves in dispersive medium with its advantage of good resolution in both time and frequency domains It makes the determination of the time arrivals of the dispersive AE waves at each local frequency more accurate [10, 11, 18,19,20]. Other advanced signal processing techniques, such as Mathematical Problems in Engineering
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