A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels. (1st September 2019) - Ebrahimkhanlou, Arvin; Dubuc, Brennan; Salamone, Salvatore
Highlights: A deep-learning framework to localize and characterize acoustic emissions. A single sensor approach for metallic panels with rivet-connected doublers. A procedure to determine the optimal number of deep learning layers and nodes. Sensitivity analyses to the number of sensors, input patterns, and output layers. Experimental validations, comprehensive comparisons, and generalization assessments. Abstract: This paper introduces a deep learning-based framework to localize and characterize acoustic emission (AE) sources in plate-like structures that have complex geometric features, such as doublers and rivet connections. Specifically, stacked autoencoders are pre-trained and utilized in a two-step approach that first localizes AE sources and then characterizes them. To achieve these tasks with only one AE sensor, the paper leverages the reverberation patterns, multimodal characteristics, and dispersive behavior of AE waveforms. The considered waveforms include AE sources near rivet connections, on the surface of the plate-like structure, and on its edges. After identifying AE sources that occur near rivet connections, the proposed framework classifies them into four source-to-rivet distance categories. In addition, the paper investigates the sensitivity of localization results to the number of sensors and compares their localization accuracy with the triangulation method as well as machine learning algorithms, including support vector machine (SVM) and shallow neural network. Moreover, the generalization of the deep learning approach is evaluated for typical scenarios in which the training and testing conditions are not identical. To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were carried out on two identical aluminum panels with a riveted stiffener. The results demonstrate the effectiveness of the deep learning-based framework for single-sensor, AE-based structural health monitoring of plate-like structures.