摘要
根据滚动轴承振动信号的频域变化特征 ,采用小波包分析对其建立频域能量特征向量 ,利用径向基函数神经网络完成滚动轴承故障模式的识别。理论和试验证明了该方法的有效性。
Based on the frequency domain characteristics of the vibration signals of the ball bearings, the characteristic vector of frequency domain energy was established using the wavelet packet analysis for the frequency domain characteristics , and the recognition of the fault models of the ball bearings was completed by using radial basis function neural network. The efficiency was proved in theory and test.
出处
《化工机械》
CAS
2004年第3期155-158,共4页
Chemical Engineering & Machinery
基金
吉林省教育委员会基金项目 (吉教合字 99第 1 0号 )。
关键词
滚动轴承
小波包分析
特征向量
神经网络径向基函数
模式识别
Ball Bearing, Wavelet Packet, Characteristic Vector, Neural Network, Radial Basis Function, Model Recognition
