摘要
齿轮是旋转机械中的关键元件。提出了一个基于支持向量机的齿轮多故障分类方法。齿轮状态被划分为正常、齿轮磨损和断齿状态。振动信号的均方根和小波包能量被选作为分类器的特征参数。分类器选用支持向量机(SVM)。SVM具有良好的实用性及多分类能力。实验结果表明:提出的方法能很好地区分齿轮故障。
Gears are critical elements in rotating machinery. An approach is proposed based on support vector machine（ SVM） to solve classification of multiple gear conditions. These conditions are divided into normal,wear,and broken teeth conditions. The root mean square（ RMS）and the wavelet packet energy at different scales of the vibration signals of gearbox casing are employed in constructing the features of classifier. SVM is employed for the classifier,and it has the abilities of multi-class classification and good generalization. The experimental results show that the proposed method is able to discriminate the gear faults clearly.
出处
《机床与液压》
北大核心
2014年第18期54-57,共4页
Machine Tool & Hydraulics
基金
Project supported by Jiangxi Province Education Department Science Technology Project(GJJ14365)
Jiangxi Province Nature Science Foundation(20132BAB201047,20114BAB206003)
关键词
齿轮
支持向量机
故障分类
小波包能量
Gear
Support vector machine
Fault classification
Wavelet packet energy
