A Wear Type Recognition Algorithm for Ferrography Images Based on CBAM-EfficientNet-B0

Ferrographic image wear type identification is an important method to analyze the wear failure of mechanical equipment. Aiming at the problem of low classification accuracy caused by the small number of samples in the abrasive particle dataset and the small differences in texture, shape and color of different wear types, a wear type recognition algorithm based on improved EfficientNet network was proposed. In this paper, EfficientNet-B0 is selected as the basic model for wear type recognition, and the CBAM attention module is integrated into EfficientNet-B0 to construct CBAM-EfficientNet-B0, thereby improving the focusing ability and information expression ability of abrasive particles. In this paper, a dataset of abrasive grain images for five types of wear is con-structed. The wear type recognition ability of CBAM-EfficientNet-B0 is tested on the test dataset. The experimental results show that the accuracy of the wear type identification algorithm CBAM-EfficientNet-B0 proposed in this paper is 92.55%, which is 2.51% higher than that of the Effi-cientNet-B0 algorithm before the improvement, which improves the accuracy and efficiency of mechanical equipment wear state identification. Comparing CBAM-EfficientNet-B0 with MobilenetV3, Resnet50, VGG16 and ViT classification models, the experimental results show that the precision, recall and accuracy of CBAM-EfficientNet-B0 are higher than other methods in the com-parative experiments. This research provides new technical options for condition maintenance and fault diagnosis of equipment.