基于核函数估计的转子故障诊断方法

研究核函数估计方法(KFA)在机械故障诊断中的应用问题，提出一种基于特征样本选择的转子故障模式分类方法。通过计算转子振动信号原始特征空间的内积核函数，将所有原始特征样本映射到高维特征空间，在高维空间中选择特征样本对转子裂纹、转子不平衡及转子碰摩三种故障模式进行分类识别，选择的特征样本远小于样本集中全体样本的数目，提高了运算速度。比较了KFA分类方法与支持矢量机(SVM)分类方法的效果，结果表明，在保证分类精度的条件下，KFA方法可以明显减少运算量，性能更优越。

ROTOR FAULT DIAGNOSIS METHOD BASED ON KERNEL FUNCTION APPROXIMATION

Kernel function approximation is investigated together with some applications in mechanical fault diagnosis, and an approach to rotor fault classification based on feature samples selection is presented. The integral operator kernel functions is used to realize the nonlinear map from the raw feature space of rotor vibration signals to high dimensional feature space, where appropriate feature samples are selected to classify three kinds of rotor faults: rotor crack, rotor unbalance and rotor rub. The quantity of selected samples is much less than that of whole sample sets, which has quickly expedited the computation process. The classification result of KFA is compared with that of SVM. It can be seen that the classification accuracy of KFA is fairly as well as that of SVM, and KFA is or even better than SVM in terms of computation load.