基于弗德卡曼阶比跟踪的转子起停车故障特征提取方法及应用

Fault feature extraction from the vibration signals in rotor start-up or slowdown processes based on order tracking and holospectrum

针对传统起停车过程分析采用短时傅里叶变换提取瞬时幅值及相位会损失瞬变信息的不足,用弗德卡曼阶比跟踪原理(Vold-Kalman Filter Based Order Tracking,VKF-OT)结合全息谱原理,提出新的转子起停车故障特征提取方法。由转子起停车瞬态响应数据中提取随转速变化的阶比分量,通过各阶分量复包络直接求幅值、相位,能克服傅里叶变换的平均效应,保留转子振动瞬变信息;通过VKF-OT集成转子截面振动信息,结合全息谱理论绘制阶比全息瀑布图,提取转子起停车状态的故障特征,并用于起停车瞬态动平衡。结果表明,该方法可有效提取转子典型故障特征、降低转子系统一阶临界振动。

The vibration signal in start-up or slowdown processes can be regarded as a dynamic response to a wide frequency range excitation, which contains more information than steady state vibration signals. So it deserves more attention to extract fault feature from the signals collected in the start-up or slowdown processes of a rotor system. Aiming at the defect that a conventional start-up or slowdown process analysis applying STFF to get vibration amplitude and phase will usually lose transient information, a method based on Vold-Kalman filtering order tracking （VKF-OT） and holospectrum was proposed. By the method, the vibration amplitude and phase can be obtained directly from the complex envelop of each order component, so they can preserve the transient information well and get rid of the average effect of STFF. The amplitudes and phases of the vibrations in two perpendicular directions at a rotor section were measured and a holo-watefall curve was plotted according to the holospectrum theory. On this basis, the fault feature extraction and transient balancing can be fulfilled. The experiment results show that the method can extract the typical fault features of the rotor and the transient balancing can also be well achieved.