关键词: 机械制造工艺与设备, 滚动轴承, 故障诊断, K-L变换, 支持向量机, 模式识别

Abstract: A fault diagnosis approach for the rolling bearing based on the KL transform(KLT) and support vector machine(SVM) was proposed. The KLT can transform a multidimensional correlated variable into a less dimensional independent eigenvector, and SVM enables the pattern recognition and nonlinear regression. Based on these concepts, the KLT was employed to extract the state eigenvector from the bearing vibration signals, then the SVM classifier was established to recognize the fault pattern. Experimental results showed that the combination of the eigenvector decomposed by the KLT with the SVM is able to recognize the fault patterns of the rolling bearing effectively and accurately, providing a new approach to the development of fault diagnosis intelligentization.

Key words: mechanical manufacturing engineering and equipment, rolling bearing, fault diagnosis, K-L transform, support vector machine, pattern recognition