Research on Defect Category Identification Method for Rough Surface Thick Wall Steel Plate Based on Autoencoder-BP Neural Network

For the processing of echo signals in defect detection, wavelet transform and principal component analysis are mostly used to extract features. However, the feature values obtained by these methods often lead to redundancy, resulting in the waste of a lot of resources for defect identification. This paper, in the context of defects in thick-walled steel plates with rough surfaces, proposes a defect category recognition classification method based on an autoencoder-BP neural network. It uses signals from electromagnetic ultrasonic and pulsed eddy current composite detection as the neural network learning signals. Impedance analysis is used to more comprehensively reflect the characteristics of defects, thereby improving the accuracy of defect identification. Autoencoder is selected to extract the geometric features of the composite detection signals, which can effectively extract useful features from the dataset. The feature dataset is then divided into training and testing sets. Simulation experiments show that the trained neural network model has achieved a classification accuracy of 90.8% in the testing.