An effective data enhancement method of deep learning for small weld data defect identification

Abstract

Welding has become one of the most important manufacturing technology. Due to the operating environment, workmanship and welding parameters, different welding defects will inevitably appear in the welding process. In order to effectively identify these defects, X-ray films based on nondestructive testing are usually used. Owing to the small number of X-ray film samples, this paper proposes an attention self supervised learning auxiliary classifier generative adversarial net (ASSL-ACGAN) algorithm to expand the samples to improve the defect identification of small sample data sets. In addition, the influence of data transformation preprocessing on the sample quality of ASSL-ACGAN is also studied. Finally, intelligent defects identification based on transfer learning on two data sets is carried out. Experimental results not only suggest that ASSL-ACGAN based data enhancement is superior to wasserstein GAN (WGAN), WGAN gradient penalty (WGAN-GP) and auxiliary classifier generative adversarial net (ACGAN), but also prove the identification accuracy of ASSL-ACGAN exceeds that on original data set, with an average of 2.79%. The paper provides a possible scheme for defect identification of small number samples.