Internal defects inspection of arc magnets using multi-head attention-based CNN

Abstract

Arc magnets are the key components of various motor machinery, and their internal defects detection is extremely significant for maintaining system performance and ensuring operational safety. In this paper, an end-to-end improved convolutional neural network (CNN) model based on multi-head attention is presented, where features that play a more important role in defect detection could be efficiently highlighted. In addition, owing to the characteristics of strong parallel working ability in multi-head attention, the training process is greatly accelerated. Meanwhile, to meet the requirements of the model on the amount of data, a data augmentation method is designed accordingly. Then, the performance of the constructed framework is verified in different test scenarios. Experiment results demonstrate that the presented approach owns superior inspection performance based on relatively fewer model parameters compared to other existing methods, even under the small sample, intense noise, and the coexistence of noise and insufficient data.