基于雷达图分形特征先验卷积网络的人工关节磨屑智能识别

doi:10.3901/JME.260296

摘要/Abstract

摘要： 人工关节磨屑产生于关节配副的摩擦运动中，携带着大量微观尺度上的磨损信息。然而目前对于人工关节磨屑的研究以人工识别为主，缺乏客观的评定标准。关节磨屑的分类识别属于图像识别的领域，卷积神经网络等深度学习算法的发展使图像识别的精度进一步提升。因此，为解决人工识别工作量大和客观性缺乏的问题，建立了基于分形特征先验的卷积神经网络识别模型对磨屑进行智能识别。首先基于改进的雷达图方法计算磨屑的分形维数，然后将分形维数作为固定特征进行先验判定，判定后的结果导入卷积神经网络模型进行分类。同时结合数据增强技术以扩充数据集，平衡类间分布不均。结果表明雷达图计算的分形维数特征与磨屑形状分布具有较好的关联性，分形维数先验判定和数据增强技术能够提高深度网络的识别精准度，基于AlexNet和ResNet网络的模型识别准确率分别达到83.70%和89.26%，高于AlexNet和ResNet网络原始模型的80.74%和82.59%。此外，基于AlexNet和ResNet网络的模型在检验数据集上的准确率分别为86.60%和88.66%，接近训练数据，表现出良好的泛化能力。

关键词: 人工关节磨屑, 雷达图分形维数, 卷积神经网络, 智能识别

Abstract: Artificial joint wear debris generate from the frictional motion of joint pairs, carrying mount of micro scale wear information. However, most of the artificial joint wear debris research is mainly by manual recognition, which lacks objective evaluation criteria. Classification and recognition of wear debris belongs to the image recognition field, intelligent classification such as convolutional neural network may solve the drawbacks of manual analysis. Therefore, radar graph fractal feature prior convolutional network is constructed to realize the intelligent recognition of artificial joint wear debris. Firstly, fractal dimension values of wear debris are calculated by the improved radar method, and the fractal dimension is used as the fixed characteristic of the wear debris to perform prior judgment. The judgement results are imported into the convolutional neural network models AlexNet and deep residual network (ResNet) to recognize the wear debris class. The models are trained in the wear debris picture dataset collected from literatures, and the generalization ability of the models is verified in the experimental dataset. The data augmentation method is also employed to expand the dataset and balance the uneven distribution among debris classes. Moreover, to compare the performance difference between the convolutional neural networks and ordinary machine learning models, a support vector machine (SVM) model is also used to classify the debris classes for the training data. The results showed that the radar graph fractal dimension had a good correlation with the shape distribution of the wear debris. The recognition accuracy and precision of convolutional neural network models are significantly higher than that of SVM. The fractal dimension prior judgment and data augmentation can obviously improve the recognition accuracy of the original model. The recognition accuracy of models based on AlexNet and ResNet networks reach 83.70% and 89.26%, respectively, which is higher than 80.74% and 82.59% of the AlexNet and ResNet original models of networks. The recognition accuracy models based on AlexNet and ResNet networks on the validation data are 86.60% and 88.66% respectively, which are close to the models on the training data, showing good generalization ability.

Key words: artificial joint wear debris, radar graph fracture dimension, convolutional neural network, intelligent identification

中图分类号:

TH117
R684

张卫鹏, 谷永庆, 罗勇, 渠璐平. 基于雷达图分形特征先验卷积网络的人工关节磨屑智能识别[J]. 机械工程学报, 2026, 62(9): 254-267.

ZHANG Weipeng, GU Yongqing, LUO Yong, QU Luping. Intelligent Recognition of Artificial Joint Wear Debris by Radar Graph Fractal Feature Prior Convolutional Network[J]. Journal of Mechanical Engineering, 2026, 62(9): 254-267.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I9/254

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