基于核函数估计的转子故障诊断方法  

ROTOR FAULT DIAGNOSIS METHOD BASED ON KERNEL FUNCTION APPROXIMATION

作　　者：李巍华[1] 史铁林[2] 杨叔子[2] 

机构地区：[1]华南理工大学汽车工程学院,广州510640 [2]华中科技大学机械工程学院,武汉430074

出　　处：《机械工程学报》2006年第9期76-82,共7页Journal of Mechanical Engineering

基　　金：国家重点基础研究973计划(2003CB716207);国家自然科学基金(50475095);广东省自然科学基金(05300143;04020082);振动;冲击与噪声国家重点实验室开放基金(VSN-2004-03);广东省电动汽车重点实验室开放基金(E4060110)资助项目

摘　　要：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。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.

关 键 词：核函数 特征选择 故障分类 转子 

分 类 号：TG156[金属学及工艺—热处理]