Aiming at the difficulty of classification of pipeline weld defects,a welding defect classification method using piezoelectric sensor data,combined with Gramian Angular Field(GAF)and Residual Neural Network(ResNet)was proposed.Firstly,the GAF principle is used to convert one-dimensional time series data into two-dimensional images,and the converted two-dimensional image data set is used as input to train the optimal two-dimensional residual neural network model for weld defect classification.In the experiment,10 defects(5 types)were prefabricated in the pipeline weld.Guided wave and ultrasonic technology were used to detect the 1-5 defects in the weld respectively,and the three indicators of Precision,Recall and F1-score were analyzed.The feasibility of the GAF-ResNet method,and defects 6-10 verify the reliability and universality of the method.
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