The application of intelligent bearing surface defect classification based on deep neural networks remains challenging in real factories, due to the scarcity of defect samples. Under real-working conditions, with less training samples, the paper proposes a few-shot bearing defect image classification network which can recognize different bearing surface defects image, including notch, reddish rust, scratching, incising, conformity, pitting and mill scale. Based on general metric learning neural network framework, a local feature extraction layer is designed, which calculates the auto-correlation vector of global feature in a sliding region to enhance detail features. Additionally, a similar feature attention module emphasizes the the regions of similarity between the query set and the class prototype center to overcome the influence of background noise on classification. To validate the effectiveness of the proposed network, comparative experiments were conducted using the benchmark dataset miniImageNet, achieving classification accuracies of 59% in the 5-way 1-shot setting and 76% in the 5-way 5-shot setting respectively. Furthermore, to assess its performance in a real-factory condition, a self-made dataset of bearing defects from a factory was employed. The proposed network achieved a remarkable classification accuracy of 88% in the 5-way 5-shot setting. These experimental results confirm the practical application value of our few-shot bearing surface defect image classification network, demonstrating its ability to accurately recognize various bearing defects with limited training samples.
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