HHT改进及其在风力发电机故障诊断中的应用

Improved HHT and Its Application in Wind Turbine Bearing Fault Diagnosis

为解决风力发电机轴承故障特征难以提取的问题,提出一种基于相关分析的改进的HHT方法。该方法利用EMD是近似正交分解的特征,将EMD产生的残差分量与原信号间的相关系数作为阈值,对IMF进行自适应筛选,解决了由三次样条拟合误差引发的伪IMF问题。通过仿真分析对该方法进行了验证。使用该方法分析D70型1.5 MW风力机承振动信号,诊断出了轴承故障,进而可以将该方法应用于工程实践中。

To solve the problems in extracting wind turbine bearing fault features,the improved HHT based on correlation analysis is proposed. By using EMD,an approximately orthogonal decomposition method,the correlation coefficient between the residual component and the original signal is taken as a threshold to adaptively filter IMF,solving the problem of the pseudo-IMF caused by cubic spline fitting error. Simulation analysis has verified this method. This method was employed to analyze the bearing vibration signal of D70 1. 5 MW wind turbine,and diagnose bearing faults. Thus the method can be applied to engineering practice.