Recognition Method of Pipeline Weld Defects Based on Auxiliary Classifier Generative Adversarial Networks

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This study aims to address the problem of poorly generated data quality and slow network training speed in data augmentation of 1-dimensional time domain signals. A data augmentation method for the generative adversarial network based on the time-frequency graph of magnetic flux leakage (MFL) signals is proposed. First, the original MFL is converted into a time-frequency signal graph, which is suitable for the input data of the conventional neural network. Second, to produce an adversarial network combined with auxiliary classification, the label of the original MFL signal is used as the input of this network for data augmentation. Compared with a 1-dimensional time domain signal data augmentation method, the proposed method effectively reduces the number of network training parameters and simultaneously solves the problem of slow training speed in traditional methods. Finally, the proposed method is applied to the identification of MFL signals collected by a pipeline company. The experimental results show that the proposed method can produce higher-quality data and effectively improve the accuracy of weld defect magnetic leakage signal recognition and the speed of network training, while the accuracy rate of magnetic leakage signal recognition is increased from 94.5% of the traditional method to 99.5%, which is feasible for practical engineering applications.