摘要
提出了应用K-L变换和支持向量机相结合进行滚动轴承故障诊断的方法。K-L变换可以将高维相关变量压缩为低维独立的主特征向量,而支持向量机可以完成模式识别和非线性回归。试验结果表明,利用主矢量分解后的主特征向量与支持向量机相结合可以有效、准确地识别轴承的故障模式,为轴承故障诊断向智能化发展提供了新途径。
On the basis of statistical learning theory and the feature analysis of vibration signal of rolling bearing, a new method of fault diagnosis based on K - L transformation and support vector machine is presented. Multidimensional correlated variable is transformed into low dimensional independent eigenvector by the means of K - L transformation. The pattern recognition and nonlinear regression are achieved by the method of support vector machine. In the light of the feature of vibration signals, eigenvector is obtained using K - L transformation, fault diagnosis of rolling bearing is recognized correspondingly using support vector machine multiple fault classifier. Theory and experiment shows that the recognition of fault diagnosis of rolling bearing based on K - L transformation and support vector machine theory is available to recognize the fault pattern accurately and prorides a new approach to intelligent fault diagnosis.
出处
《煤矿机械》
北大核心
2006年第6期1084-1086,共3页
Coal Mine Machinery
关键词
滚动轴承
故障诊断
K-L变换
支持向量机
rolling bearing
fault diagnosis
K- L transformation
support vector machine
