噪声与振动控制2025，Vol.45Issue(5)：110-116,7.DOI:10.3969/j.issn.1006-1355.2025.05.018
改进图注意力网络变工况滚动轴承故障诊断方法
Rolling Bearing Fault Diagnosis under Variable Working Conditions Based on DSR-GATv2
摘要
Abstract
Aiming at the problems that the rolling bearing fault is difficult to be identified since the complex working environment and the fault signal are often accompanied by higher level noise,and the traditional neural network methods have low recognition accuracy in the case of small samples,this paper proposes a method based on decorrelated spectral re-gression(DSR)combined with graph attention network v2(GATv2)for fault diagnosis of rolling bearings under variable working conditions.Firstly,multi-domain bearing vibration signal features are extracted during data processing to enrich the original feature set.Then,the DSR method is used to reduce the feature dimension,and eliminate the selection bias caused by artificial screening features.Finally,the improved graph attention network is used for fault diagnosis.The bearing dataset of Paderborn University(PU dataset)is used to test the effectiveness of the proposed method.Comparing with some classi-cal models shows that the proposed method has excellent noise immunity and high detection accuracy in the case of small samples.关键词
故障诊断/滚动轴承/去相关谱回归/图注意力神经网络/小样本Key words
fault diagnosis/rolling bearing/decorrelated spectral regression/graph attention neural network/small sample分类
机械制造引用本文复制引用
李耀华,张鑫杰..改进图注意力网络变工况滚动轴承故障诊断方法[J].噪声与振动控制,2025,45(5):110-116,7.基金项目
国家自然科学基金资助项目(U2033209) （U2033209）
