噪声与振动控制Issue(1)：138-141,162,5.DOI:10.3969/j.issn.1006-1335.2016.01.030
EMD马氏距离与SOM神经网络在故障诊断中的应用研究
Application of EMD Mahalano-bis Distance and SOM Neural Network in Fault Diagnosis
摘要
Abstract
To achieve accuracy identification of weak dynamic response and early fault diagnosis, a fault diagnosis method based on EMD Mahalano-Bis distance and SOM neural network was proposed. First of all, to improve the signal-to-noise ratio, particle filtering was conducted to the original signals of vibration. Then, the signals were decomposed by the EMD. Each intrinsic mode function was analyzed to obtain the eigenvectors which include their corresponding eigenvalues. And the eigenvectors of the original signals were found. In order to select the intrinsic mode function which can represent the signal characteristics, the Mahalano-Bis distance between the intrinsic mode functions and the eigenvectors of the original signals was calculated. The best intrinsic mode function was chosen and input to the well-trained self-organizing feature map (SOM) neural network. Finally, the faults were classified. The application examples of bearing fault diagnosis show the effectiveness of this method.关键词
振动与波/粒子滤波/EMD/马氏距离/SOM神经网络/故障诊断Key words
vibration and wave/particle filter/empirical mode decomposition(EMD)/Mahalano-bis distance method/self-organizing feature map(SOM) network/fault diagnosis分类
机械制造引用本文复制引用
姚海妮,王珍,邱立鹏,陈建国,杨铎..EMD马氏距离与SOM神经网络在故障诊断中的应用研究[J].噪声与振动控制,2016,(1):138-141,162,5.基金项目
国家自然科学基金(51405153)；辽宁省教育厅一般项目(L2012446) （51405153）
