A zero-shot quantitative evaluation model for subsurface defects size based on ultrasonic nondestructive testing

Abstract

Existing defect quantitative evaluation models requires collecting and labeling all types of defect data for training. However, such models have a poor ability for new defect, and collecting and labeling data is a costly task. A zero-shot evaluation model is proposed to solve the questions. First, a Wavelet Packet Transform-based method is developed to convert defect ultrasonic signals into time–frequency images. Second, we define defect semantics from length, width, and height to describe defects of different sizes. Third, the ResNet with embedded Brownian Distance Covariance matrix and multi-dimension parallel framework is designed to extract the image features. Then, an autoencoder is trained to embed the defect semantic into the feature space. Finally, the dimension of defect is evaluated by measuring the similarity between the features and the category centroids. Experimental results reveal that our model has better ability to evaluate the dimensions of new defect compared to others models.