谱峭度和Vold-kalman阶比跟踪在风电机组齿轮箱故障诊断中的应用

针对风电机组齿轮箱在时变工况下的振动信号具有非平稳特性,提出一种谱峭度和Vold-kalman阶比跟踪(Vold-Kalman Filter Based Order Tracking,VKF-OT)相结合的故障特征提取方法。以转频和啮合频率作为VKF-OT的提取频率,获得随转速变化的阶比信号,通过阶比信号复包络直接求两种频率分量的幅值、相位,经实验分析这种方法能保留齿轮箱的瞬变信息。而后计算两种频率分量的谱峭度,以最大谱峭度对应的频率带能量与原阶比信号总能量之比作为故障特征,最后采用高斯混合模型对风电机组齿轮箱在不同工况下的150组振动信号进行特征描述,运用最大贝叶斯分类器实现故障识别。故障识别率表明该方法可有效地识别任意时变工况下的齿轮早期局部微弱故障。

Application of Spectral Kurtosis and Vold-Kalman Filter Based Order Tracking in Wind Turbine Gearbox Fault Diagnosis

In order to deal with non-stationary vibration signal of wind turbines gearbox under time-varying conditions, the fault feature extraction method combining spectral kurtosis and Vold-Kalman Filter Based Order Tracking (VKF-OT) is put forward. By the method, after set rotation and meshing frequency as extracting frequency of VKF-OT, the order component with speed changes was extracted, then, the vibration amplitude and phase could be obtained directly from the complex envelop of each order component. The experimental analysis was carried out to prove that this method could retain the transient information of gearbox. Spectral kurtosis of two frequencies components was calculated, and the ratio of frequency band energy which corresponding Maximum spectral kurtosis and total energy of the original order signal was extracted as fault feature. Finally, the features of 150 groups of vibration signal from wind turbine gear box under different conditions was described by using Gaussian mixture model, and Maximum Bayesian classifier was used to achieve failure recognition. The amount of recognition rate indicates that this method can identify local early weak fault of gear in arbitrary time-varying conditions effectively.