首页|基于拓扑感知和双视图分类器的旋转机械故障诊断方法

基于拓扑感知和双视图分类器的旋转机械故障诊断方法

扫码查看
针对旋转机械不同工况下数据分布不同,以及故障数据稀缺使得样本类别不均衡,导致故障诊断模型性能退化这一问题,提出一种基于拓扑感知和双视图分类器的故障诊断方法.该方法以一个图卷积网络为诊断框架,提出的非参数化拓扑感知模块能自适应更新图数据拓扑结构,约束不同域数据获取近似的消息传递路径,通过图卷积网络有效提取域一致故障特征;利用二分类器和多分类器构建双视图分类器,并计算二元输出和多元输出的相似度对训练数据进行重加权,避免了类别不均衡下模型的有偏训练以及对少数类样本识别能力不强的现象.利用公开的西安交通大学齿轮故障数据集、MAFAULDA旋转机械故障数据集及自制的滑动轴承故障模拟数据进行试验.结果表明,提出的方法能有效提升类别不均衡下变工况故障诊断的性能.
Fault diagnosis method for rotating machinery based on topology perception and dual-view classifier
Here,aiming at the problem of different data distributions under different operating conditions of rotating machinery and uneven sample categories due to scarce fault data to cause performance degradation of fault diagnosis model,a fault diagnosis method based on topology perception and dual-view classifier was proposed.This method could take a graph convolutional network(GCN)as diagnosis framework,and the proposed non-parametric topology perception module could adaptively update the graph data topology structure,constrain different domain data to obtain approximate message transmission paths,and effectively extract domain consistent fault features with GCN.Binary and multiple classifiers were used to construct a dual-view classifier,calculate the similarity between binary and multivariate outputs to reweight training data,and avoid biased training of model under category imbalance and weak recognition ability for minority class samples.Experiments were conducted using publicly available gear fault datasets of Xi'an Jiaotong University,MAFAULDA rotating machinery fault datasets and self-made sliding bearing fault simulation data.The results showed that the proposed method can effectively improve fault diagnosis performance under variable operating conditions and category imbalance.

topology perceptiondual-view classifiercategory imbalancevariable operation conditionsfault diagnosis

陈子旭、余文念、杜伟涛、林正宇

展开 >

重庆大学 机械与运载工程学院,重庆 400030

重庆大学高端装备机械传动全国重点实验室,重庆 400044

重庆齿轮箱有限责任公司,重庆 402263

拓扑感知 双视图分类器 类别不均衡 变工况 故障诊断

2025

振动与冲击
中国振动工程学会 上海交通大学 上海市振动工程学会

振动与冲击

北大核心
影响因子:0.898
ISSN:1000-3835
年,卷(期):2025.44(1)