基于感测线圈和KPCA的电机轴承故障检测

在电机定子中嵌入感测线圈并结合核主元分析(KPCA)进行电机轴承的故障检测。8只线圈分别嵌入电机定子的前端和后端,通过分时复用形成6组反应电机状态的差动信号。针对6组感测信号与电机状态间的非线性特征,采用KPCA进行多变量分析。着重介绍分析了KPCA故障检测的算法、监测指标和步骤。最后,用机车变压器油泵电机为对象开展了试验研究,验证了所提电机轴承故障监测方法的有效性和正确性。

Fault Detection of Motor Bearings Based on Detection coilsand KPCA Algorithm

The embedded coils and kernel principal component analysis (KPCA) together were proposed to detect the bearing faults. 8 detection coils were firstly embedded into the front and rear ends of the motor stator, respectively; and they form 6 groups of differential signals by timesharing multiplexing. The differential signals reflect the operation status of the motor; however, the relationship between the differential signals and motor status was nonlinear. Thus, we employ KPCA to deal with the problem of nonlinearity. The algorithm, detection statistics and steps of KPCA were investigated. Finally, an oil pump was used to carry out the experimental studies. The experiment results were presented to prove the validity and correctness of the proposed fault detection method.