CNN-LSTM-Based Damage Localization of Plate Structure.

To address the challenges of feature extraction from time-domain signals and imprecise damage localization in conventional plate structure damage identification methods, this study proposes an innovative damage localization approach integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The one-dimensional signal data extracted from the aluminum plate is converted into a two-dimensional grayscale image, leveraging the advantages of CNN to accurately extract the information features of the damaged image on the aluminum plate. These extracted features are subsequently channeled into the LSTM network and the unique forgetting and memory mechanisms inherent in LSTM are employed to integrate the input feature information through a three-layer LSTM network, which is then fed into a fully connected layer for regression prediction. This method is not only an innovative application of joint deep learning methods in the field of damage detection but also accurately predicts the coordinates of the damage location, effectively overcoming the limitations of traditional damage localization methods. To validate the effectiveness of our proposed method, experiments were conducted on aluminum plates. The results demonstrate that our method shows strong performance in accurately localizing artificial damage on aluminum plates.