关键词: 振动与波, 滚动轴承, 齿轮箱, 故障诊断, KICA, GDA, LSSVM

Abstract:

In order to achieve higher fault recognition rate of gearbox rolling bearing, a fault recognition method was proposed based on KICA (kemel independent component analysis)-GDA (generalized discriminant analysis) and LSSVM (least squares support vector machine). Firstly, fault features of rolling bearing vibration signal such as kurtosis and information entropy were computed and were recognized as initial feature vectors ,which were mapped into a kernel feature space with KICA to omit the redundancy and correlation among the fault features. Then the GDA was used to implement feature fusion and new features were inputted to LSSVM classifier for fault classification. The experimental results show the KICA-GDA and LSSVM method improves LSSVM’s classification performance by KICA-GDA obtaining additional discriminative information, and has more accurately rolling bearing fault classification than direct LSSVM method.