SSCT-Net: A Semisupervised Circular Teacher Network for Defect Detection With Limited Labeled Multiview MFL Samples

Deep learning methods have demonstrated promising performance in magnetic flux leakage (MFL) defect detection under adequate amounts of labeled samples. However, in industrial occasions, obtaining adequate amounts of labeled samples is time-consuming and expensive, and applying only limited labeled samples can lead to unsatisfactory defect detection accuracy. To address the above issues, a defect detection method named semisupervised circular teacher network (SSCT-Net) is proposed in this article. First, a parallel feature extraction network with hybrid attention is proposed in SSCT-Net so that the useful features of multiview MFL signals can be extracted simultaneously. Second, semisupervised circular learning is proposed for the first time. In semisupervised circular learning, a distinguishable feature embedding space is constructed, and two structurally identical deep networks cosupervise and collaborate through the proposed consistent circular strategy so that the decision bias of unlabeled samples can be reduced. Finally, the trained model is applied for defect detection in practice. The proposed method can establish a potential connection between multiview MFL signals and fully utilize labeled and unlabeled MFL signals. The experiments in simulations and real-world applications demonstrate that SSCT-Net can reach 92% detection accuracy with only 20% labeled samples, which is more effective than the state-of-the-art methods and leads to a promising practical utility of the proposed method.

Automated summary

In this article, a defect detection method named SSCT-Net is proposed, which can effectively utilize labeled and unlabeled magnetic flux leakage (MFL) signals for defect detection by constructing a parallel feature extraction network and implementing semi-supervised circular learning. The proposed method achieves a detection accuracy of 92% with only 20% labeled samples, outperforming state-of-the-art methods and demonstrating promising practical utility.