Abstract
Guided wave is a key nondestructive technique for structural health monitoring due to its high sensitivity to structural changes and long propagation distance. However, to achieve high accuracy for damage location, large quantities of samples and thousands of iterations are typically needed for detection algorithms. To address this, in this paper, an eXplainable Convolutional neural network for Multivariate time series classification (XCM) is adopted, which is composed of one-dimensional (1D) and two-dimensional (2D) convolution layers to achieve high accuracy damage location on pressure vessels with limited training sets. By further optimizing the network parameters and network structure, the training time is greatly reduced and the accuracy is further improved. The optimized XCM improves the damage location precision from 95.5% to 98% with small samples (training set/validation set/testing set = 23/2/25) and low training epochs (under 100 epochs), suggesting that the XCM has great advantages in pressure vessel's damage location classification its potential for guided wave-based damage detection techniques in structural health monitoring.
Issue Section:
Design and Analysis
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