四川大学学报（自然科学版）2024，Vol.61Issue(4)：332-340,9.DOI:10.19907/j.0490-6756.2024.047001
基于坐标注意力关系网络的小样本轴承故障诊断
Few-shot bearing fault diagnosis based on coordinate attention relation network
摘要
Abstract
Bearing fault diagnosis is of great significance to ensure the normal operation of machinery.Nowa-days,fault diagnosis methods based on machine learning such as Alexnet,Resnet-18,Relation Network,Re-lation Network based on Channel Attention SENet(SERN)and Relation Network based on Mixed Atten-tion CBAM(CBRN)are extensively utilized.However,the performance of these methods can be seriously damaged by small samples and changing working conditions existing in practical engineering applications,and even the problem of overfitting can be resulted.In this paper,based on the coordinate attention network,a novel bearing fault diagnosis method is proposed for overcoming these problems.By embedding the coordi-nate information and generating coordinate attention,a coordinate attention relationship network is con-structed to solve the problem that the relational network model cannot be used to establish the long-distance dependence between feature map and fault feature location information,enhance the model's expression on fault features in the target region and reconstruct more discriminative fault sample features.Then,a feature embedding module is used to generate the feature vector of samples,by which the labeled samples and unla-beled samples can be spliced to generate the feature vector group.Finally,a relationship score module is used to measure the nonlinear distance of feature vector group,generate the relationship score and judge the class of unlabeled samples to achieve fault classification.Simulation results show that,comparing with the known methods,the proposed method has better classification performance on small sample bearing data sets.关键词
小样本学习/关系网络/故障诊断/坐标注意力机制/轴承Key words
Few-shot learning/Relation network/Fault diagnosis/Coordinate attention mechanism/Bearing分类
机械工程引用本文复制引用
郭敏,陈鹏,周超,胡国宾,范青荣..基于坐标注意力关系网络的小样本轴承故障诊断[J].四川大学学报（自然科学版）,2024,61(4):332-340,9.基金项目
国家重点研发计划(2022YFB4701500) （2022YFB4701500）
