基于小波包与改进BP神经网络的齿轮故障诊断  

Fault diagnosis of gear based on wavelet package and improved BP neural network

作　　者：时建峰[1] 时军[2] 时伟[1] 周明[1] 李增贺[1] 

机构地区：[1]石家庄学院,河北石家庄050035 [2]北京市第十中学,北京100072

出　　处：《机械研究与应用》2011年第1期82-84,87,共4页Mechanical Research & Application

摘　　要：对齿轮振动信号应用小波包分解提取故障特征向量,并以此作为改进BP神经网络的输入,对神经网络进行训练,建立齿轮运行状态分类器,用以诊断齿轮的运行状态。结果表明,该方法对齿轮故障诊断十分有效。The wavelet package is applied to decompose vibration signal of gear to get fault eigenvector.Then,the eigenvector is employed as the input samples to train the improved BP neural network,so as to set up the classifier of operational state to identity gearbox fault.The experimental results testify that the proposed method is a useful tool to diagnose gear fault.

关 键 词：小波包 BP神经网络 齿轮 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]