A Multidimensional Data Fusion Neural Network for Damage Localization Using Ultrasonic Guided Wave

Ultrasonic guided wave (UGW)-based damage localization on plate-like composite structures plays a vital role in the structural health monitoring (SHM) of aircraft structures. Precisely locating the damage requires full utilization of high-dimensional UGW signals as well as low-dimensional transducer coordinates. However, current deep learning (DL)-based methods cannot incorporate transducer coordinates in the neural networks. To address this issue, this article proposes a novel multidimensional data fusion neural network framework for damage localization on plate-like composite structures using UGW. The proposed framework includes an encoder and a Fourier feature projection head to integrate high-dimensional wave signals and low-dimensional coordinates. A multilayer perceptron (MLP) is adopted as a decoder to learn features from the encoder and the projection head. Comprehensive experiments demonstrate that the proposed method achieves the state-of-the-art results with less than 2 mm absolute distance error. Moreover, a discussion regarding data availability in the training process is performed. The proposed method demonstrates superior robustness over the state-of-the-art methods with limited training data.