摘要
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。针对磨粒数据集样本少且不同磨损类型在质地、形状和颜色上的差异微小导致的分类精度低的问题,提出一种基于改进EfficientNet网络的铁谱图像磨损类型识别算法。本文选择EfficientNet-B0作为磨损类型识别的基本模型,将CBAM注意力模块融合到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准确率为92.55%,较改进前EfficientNet-B0算法的准确率提升了2.51%,提高了机械设备磨损状态识别精度和效率。将CBAM-EfficientNet-B0与MobilenetV3、Resnet50、VGG16和ViT分类模型进行比较,实验结果表明CBAM-EfficientNet-B0的精确率、召回率和准确率均高于对比实验中的其他方法。该研究为设备的状态维修和故障诊断提供了新的技术选择。
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 constructed. 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 EfficientNet-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 comparative experiments. This research provides new technical options for condition maintenance and fault diagnosis of equipment.
出处
《图像与信号处理》
2022年第3期101-112,共12页
Journal of Image and Signal Processing
