多尺度图池化特征融合的集成智能故障诊断方法  

Ensemble intelligent fault diagnosis method based on multi-scalegraph pooling feature fusion

作　　者：张亚军 潘东辉 张先杰 张海峰 钟凯[1,2] 刘永斌 ZHANG Ya-jun;PAN Dong-hui;ZHANG Xian-jie;ZHANG Hai-feng;ZHONG Kai;LIU Yong-bin(Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education,Anhui University,Hefei 230601,China;Institutes of Physical Science and Information Technology,Anhui University,Hefei 230601,China;School of Mathematical Sciences,Anhui University,Hefei 230601,China;School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230601 [2]安徽大学物质科学与信息技术研究院,安徽合肥230601 [3]安徽大学数学科学学院,安徽合肥230601 [4]安徽大学电气工程与自动化学院,安徽合肥230601

出　　处：《振动工程学报》2024年第12期2148-2157,共10页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(61973001,52075001);安徽省自然科学基金资助项目(2208085QF205);安徽省高校自然科学研究重点项目(2022AH050097,KJ2021A0071)。

摘　　要：图神经网络模型由于其丰富的故障表征能力,已在故障诊断领域得到广泛应用。然而现有模型在处理故障数据时仅利用相邻节点间的局部信息,未能充分提取全局特征信息,为了克服单一模型故障诊断精度不高和泛化能力不足的问题,提出一种基于多尺度图池化特征融合与图卷积网络(MSGP-GCN)的集成故障诊断方法。通过对原始信号构建图模型,使用图池化粗化得到全局信息。根据节点的度在不同尺度下分配权重,进而利用全局信息结合权重更新节点特征。将更新后的节点特征分别输入不同的分类器中,对分类结果使用多数投票策略实现智能故障诊断。在SEU仿真数据集和真实的磨煤机数据集上对所提出的方法进行验证,结果表明所提模型能够明显提高故障诊断的精度和泛化能力,平均诊断精度分别达到98.31%和97.21%。The graph neural network models have been widely used in the field of fault diagnosis due to the advantage of abundant fault characterization capabilities.However,the existing models only utilize the local information among neighboring nodes when dealing with fault data,and fail to fully extract the global feature information.Meanwhile,in order to overcome the problems of low accuracy and insufficient generalization ability of single model.This paper proposes an ensemble method with multi-scale graph pooling feature fusion and graph convolutional network(MSGP-GCN).The graph model is constructed from the original signal,and global information is obtained using graph pooling coarsening.Then weights are assigned at different scales based on the degree of the nodes,and the global information is used to update the node features in combination with the weights.The updated node features are input into different classifiers respectively,and the intelligent fault diagnosis result is obtained by majority voting strategy among these classification results.The proposed approach is fully verified by two fault datasets,the SEU simulation dataset and the real coal mill dataset.The experimental results show that the proposed model can effectively improve fault diagnostic accuracy and generalization ability in aforesaid two real datasets,and the average diagnostic accuracy reaches 98.31%and 97.21%,respectively.

关 键 词：故障诊断 全局信息 图池化 图卷积网络 多数投票策略 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33