Chen, Y, Shen, H, Zhang, G, Dong, M and Wan, X (2024) Identification of weld defects from ultrasonic signals using GASF and an improved DCGAN-ResNet network. Nondestructive Testing and Evaluation. pp. 1-27. ISSN 1058-9759

Text Identification of weld defects from ultrasonic signals using GASF and an improved DCGAN-ResNet network.pdf - Accepted Version Restricted to Repository staff only until 9 August 2025. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB)

Abstract

In this paper, a method for weld defect identification from ultrasonic signals using the Gramian Angular Summation Field (GASF) and an improved deep convolutional generative adversarial network and residual network (DCGAN-ResNet) is proposed to overcome the problems of small-sample imbalance of echo signals as well as the low identification accuracy and poor efficiency of traditional convolutional neural networks (CNN). Firstly, the DCGAN model is improved based on Wasserstein distance and spectral normalization, and the augmented dataset is used to validate its effectiveness. Then, the residual block for the ResNet model is improved using group convolution to enhance the nonlinear representation of the network and reduce the number of parameters and computations. Finally, the squeeze-and-excitation (SE) attention mechanism is introduced for feature recalibration to enhance attention to important features and recognition efficiency. Experimental results show that the improved DCGANResNet method outperforms other commonly used methods in terms of feature extraction, recognition accuracy and efficiency for weld defects, and its test accuracy reaches 91.99%, which is 14.36% higher than that before dataset augmentation. Thus, the proposed method is effective and feasible for weld defect recognition from ultrasonic signals under small-sample imbalance conditions, and can also be applied to other pattern recognition fields.

Item Type:	Article
Additional Information:	This is an Accepted Manuscript version of the following article, accepted for publication in Nondestructive Testing and Evaluation. Chen, Y., Shen, H., Zhang, G., Dong, M., & Wan, X. (2024). Identification of weld defects from ultrasonic signals using GASF and an improved DCGAN-ResNet network. Nondestructive Testing and Evaluation, 1–27. https://doi.org/10.1080/10589759.2024.2387757. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (Deed - Attribution-NonCommercial-NoDerivatives 4.0 International - Creative Commons ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Uncontrolled Keywords:	0912 Materials Engineering; 0913 Mechanical Engineering; Acoustics
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TN Mining engineering. Metallurgy
Divisions:	Engineering
Publisher:	Taylor and Francis Group
SWORD Depositor:	A Symplectic
Date Deposited:	14 Aug 2024 09:48
Last Modified:	14 Aug 2024 09:48
DOI or ID number:	10.1080/10589759.2024.2387757
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23947

View Item