FDM和RCMDE结合的特征提取与故障诊断  

Feature Extraction and Fault Diagnosis Based on FDM and RCMDE

作　　者：左红艳[1,2] 刘晓波[1,2] 洪连环 ZUO Hongyan;LIU Xiaobo;HONG Lianhuan(School of Aeronautical Manufacturing Engineering,Nanchang Hangkong University Nanchang,330063,China;School of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics Nanjing,210016,China)

机构地区：[1]南昌航空大学航空制造工程学院,南昌330063 [2]南京航空航天大学机电学院,南京210016

出　　处：《振动．测试与诊断》2021年第3期539-546,624,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51365040);博士启动基金资助项目(EA202003380)。

摘　　要：为提取有效特征向量以实现航空发动动机转子的故障诊断,针对航空发动机转子振动信号的非线性、非平稳的特性,首先,应用傅里叶分解方法(Fourier decomposition method,简称FDM)提取航空发动机转子信号的边际谱重心及最大能量层的谱重心;其次,计算振动信号的精细复合多尺度散布熵;最后,应用双阶自适应小波聚类方法对特征空间实现故障分类与识别。应用航空发动机转子试验器采集的样本验证表明,上述方法提取的特征值准确且波动小,同种故障类型的特征值集中,不同故障类型之间差异大,有利于提高多种故障类型混合的诊断精度。Feature extraction is particularly important for diagnosing the faults of aero-engine rotor.Based on the nonlinearity and nonstationarity of vibration signal of aero-engine rotor,first of all,Fourier decomposition method(FDM)is applied to signal of aero-engine rotor.The marginal spectrum centroid and the power spectral centroid of maximum energy layer are extracted.Then the refined composite multiscale dispersion entropy(RCMDE)of vibration signal is calculated.Finally,the method of two-stage adaptive wavecluster is applied to fault classification and recognition of eigenvector space.Through the sample verification of the aero-engine rotor test rig,it is shown that the extracted characteristic vectors are accurate and fluctuated little,the value of the same fault type is concentrated,and the difference between different fault types is large,which are helpful to improve the diagnosis accuracy of multiple-faults.

关 键 词：傅里叶分解 精细复合多尺度散布熵 双阶自适应小波聚类 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]