摘要
强噪声干扰下滚动轴承状态监测信号呈非稳态多分量信号特点,单个传感器信号包含的故障信息有限,无法全面表征设备运行状态。提出一种基于自适应残差图注意力卷积神经网络(Re⁃sidual Graph Attention network,ResGAT)的多传感器融合故障诊断方法,利用多传感器监测信号可准确辨识不同工况下的滚动轴承故障信息。首先,将多个传感器采集的振动信号利用变分模态分解(Variational Mode Decomposition,VMD)和小波包分解(Wavelet Packet Decomposition,WPD)分解为小波系数矩阵,基于半径图策略构造包含多传感器网络信息的图结构数据;其次,基于残差网络的短接特性,设计一种自适应残差图注意力卷积网络(ResGAT),利用网络的输出及其残差,深度挖掘多传感器融合数据冗余故障信息;最后,将所提ResGAT模型应用于定转速、变转速、复合故障3种不同工况下的滚动轴承故障诊断数据集。研究结果表明,与现有方法相比,所提方法在3种工况下均具有更高的分类准确率和鲁棒性。
The rolling bearing condition monitoring signal under strong noise interference is characterized by non-stationary multi-component signals,and the fault information contained in a single sensor signal is limited,which cannot fully characterize the operating state of the equipment.This study proposed a multi-sensor fusion fault diagnosis method based on the adaptive residual graph attention convolutional neural network(ResGAT),which uses multiple sensor monitoring signals to accurately identify the rolling bearing fault information under different working conditions.Firstly,the vibration signals collected by multiple sensors were decomposed into wavelet coefficient matrices by variational mode decomposition(VMD)and wavelet packet decomposition(WPD),and the graph structure data containing multi-sensor network information was constructed based on the radius graph strategy.Secondly,based on the short-circuit characteristics of the residual network,an adaptive ResGAT was designed,which used the output and residual of the network to deeply mine the redundant fault information of multi-sensor fusion data.Finally,the proposed ResGAT model was applied to rolling bearing fault diagnosis datasets under three different working conditions:constant speed,variable speed,and composite fault.The research results show that compared with existing methods,the proposed method has higher classification accuracy and robustness under three working conditions.
作者
辛玉
闵洋
宋李俊
马婧华
周宝成
Xin Yu;Min Yang;Song Lijun;Ma Jinghua;Zhou Baocheng(School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China;Green Aviation Technology Research Institute,Chongqing Jiaotong University,Chongqing 401120,China)
出处
《机械传动》
北大核心
2024年第12期149-157,共9页
Journal of Mechanical Transmission
基金
重庆市教育委员会科学技术研究项目(KJQN202101119)
重庆市博士“直通车”科研项目(CSTB2022BSXM-JCX0163)
国家自然科学基金项目(52205144)。
关键词
多传感器融合
图神经网络
滚动轴承
故障诊断
小波包分解
Multi-sensor fusion
Graph neural network
Rolling bearing
Fault diagnose
Wavelet packet decomposition
