关键词: 振动与波, 滚动轴承, 经验模态分解, 特征能量, 故障识别, 支持向量机

Abstract: For nonlinear mapping relationship between vibration signal and state information in rolling bearing, a bearing feature vector extraction based on intrinsic mode function (IMF) feature energy in combination with support vector machine (SVM) is proposed for fault pattern recognition of bearing. The vibration signal of rolling bearing is decomposed into some IMF reflected bearing fault information by empirical mode decomposition(EMD), the energies including major information IMF are taken as eigenvectors. They are input into SVM classifier respectively for achieving fault recognition of rolling bearing. The normal state，outer race fault, inner race fault and rolling fault are simulated and tested. Results show that this method can recognize bearing fault effectively and accuracy.

Key words: vibration and wave, rolling bearing, empirical mode decomposition (EMD), feature energy, fault recognition, support vector machine (SVM)