现代信息科技2026，Vol.10Issue(10)：122-127,6.DOI:10.19850/j.cnki.2096-4706.2026.10.022
基于物理引导双分支网络的轴承故障诊断研究
Research on Bearing Fault Diagnosis Based on Physics-guided Bi-branch Network
摘要
Abstract
To address the challenge in current rolling bearing fault diagnosis where single vibration signal representations struggle to stably extract mechanism-related features,this paper proposes a Physics-Based Guided Bi-Branch Network(PBINet)to enhance fault feature recognition performance during fault diagnosis.The proposed method takes the raw time-domain signal and a transformed envelope spectrum as inputs,uses two convolutional branches to extract impact features and band-structure features,respectively,and introduces a gated fusion mechanism to achieve channel-adaptive weighting,thereby completing complementary feature fusion and discriminative learning.Experiments conducted on the CWRU and uOttawa datasets show that the proposed method achieves diagnostic accuracies of 99.2%and 100%,respectively.The results indicate that the proposed method can realize high-accuracy fault diagnosis under complex operating conditions.关键词
滚动轴承/故障诊断/特征融合/双分支网络Key words
rolling bearing/fault diagnosis/feature fusion/bi-branch network分类
信息技术与安全科学引用本文复制引用
韩世杰..基于物理引导双分支网络的轴承故障诊断研究[J].现代信息科技,2026,10(10):122-127,6.基金项目
2024年浙江工业职业技术学院校级高层次教学质量管理项目 （）
2024年浙江工业职业技术学院校级高层次教学建设培育项目 （）
