Classification and recognition of welded pipe defects based on convolutional neural networks

Aiming at the problem that conventional eddy current testing impedance plane analysis method could not identify the types of defects in stainless steel welded pipes, an effective method based on eddy current testing technology combined with machine learning was proposed to classify and identify defects in stainless steel welded pipes. Firstly, performed a short-time Fourier transform on the extracted eddy current signal to convert the original eddy current signal into a two-dimensional time-frequency map. Then input the two-dimensional time-frequency map into the input layer of the VGG-16 and GoogLeNet neural network training models. The results show that the VGG-16 and GoogLeNet neural network training models could successfully identify defects in stainless steel welded pipes, and the overall classification accuracy of the VGG-16 model was higher than that of the GoogLeNet model at a learning rate of 0.01.