Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

Abstract

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.