基于SWD-RCMFE混合算法的轴承故障诊断研究

Research on bearing fault diagnosis based on SWD-RCMFE hybrid algorithm

针对轴承振动信号中含有大量噪声信号问题,提出了SWD-RCMFE混合算法的轴承故障诊断方法。运用群分解算法对原信号分解,利用精细复合多尺度模糊熵算法及SVM提取诊断特征向量。对西储大学轴承实测信号分析验证,表明该方法对轴承的故障诊断平均识别准确率高达92%。

On the problem of abundant noise signal in bearing vibration signal,SWD-RCMFE hybrid algorithm of the bearing fault diagnosis method is proposed.The original signal is decomposed by SWD algorithm,and the eigenvector is extracted and identified through RCMFE and SVM.The analysis and verification of the measured signals of bearings in Western Reserve University show that the average recognition accuracy of the method for bearing fault diagnosis is 92%.