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

胜慧 刘
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Abstract

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.
基于CBAM-EfficientNet-B0的铁谱图像磨损类型识别算法
铁谱图像磨损类型识别是分析机械设备磨损故障的重要方法。针对磨料颗粒数据集中样本数量少、不同磨损类型的纹理、形状、颜色差异小导致分类精度不高的问题,提出了一种基于改进的effentnet网络的磨损类型识别算法。本文选择EfficientNet-B0作为磨损类型识别的基础模型,将CBAM注意模块集成到EfficientNet-B0中,构建CBAM-EfficientNet-B0,从而提高磨粒的聚焦能力和信息表达能力。本文构建了五种磨损类型的磨粒图像数据集。在测试数据集上测试了CBAM-EfficientNet-B0的磨损类型识别能力。实验结果表明,本文提出的磨损类型识别算法CBAM-EfficientNet-B0的准确率为92.55%,比改进前的Effi-cientNet-B0算法提高了2.51%,提高了机械设备磨损状态识别的精度和效率。将CBAM-EfficientNet-B0与MobilenetV3、Resnet50、VGG16和ViT分类模型进行对比实验,结果表明,在精密度、查全率和正确率方面,CBAM-EfficientNet-B0均高于其他方法。该研究为设备状态维护和故障诊断提供了新的技术选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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