基于支持向量机的旋转机械故障诊断

把支持向量机应用于诊断旋转机械不平衡和转静碰摩故障，利用转子故障实验器分别对多项式和径向基核函数进行了实验比较，选取了不同振动参数作为特征量输入支持向量机进行学习和测试。结果表明．两种不同核函数的支持向量机在各种条件下所获得的最优故障诊断准确率很接近。这说明支持向量机的性能对结构（核函数）的依赖性很小，便于在工程中应用，但特征量的选取对故障诊断准确率影响很大。对于诊断不平衡和转静碰摩故障．一、二和三阶正、反进动量是最适合的故障诊断特征量。用正、反进动量构造出SV-进动图，可明确、形象地显示故障分类面，有助于诊断故障。

Application of Support Vecter Machine to Fault Diagnosis of Rotation Machinery

an investigation into the theoretical basis of support vector machine (SVM)and its application to detect unbalance and rotor/stator rub in rotating machinery is carried out on a test rig. An experimental comparison of SVMs respectively based on two kernel functions, polynomial and radial basis functions. is made, and different signature quantities of vibration signals are inputted into SVM as source information. The results show that the optimum accuracy of fault diagnosis by both SVMs is almost identical and the performance of SVMs lessly depend on the structures (kernel functions), which makes SVMs easier to be applied in practice. However, the selection of signature signals inputted into SVMs as training data influences the accuracy of fault diagnosis markedly. For detecting unbalance and rotor/stator rub, 1x, 2x and 3x forward and backward whirls are the optimum signature signals. Additionally, the forward and backward whirls can be used to constitute SV-Whirl Graph to recognize rotor unbalance and stator/rotor rub clearly and visually.