双馈风力发电机轴承的早期诊断

Incipient Bearing Fault Detection in DFIG-Based Wind Turbines

双馈风力发电机轴承的状态监测和早期故障诊断可以及时发现轴承故障,有效地降低风电场的运行和维护费用。首先用加速度传感器采集轴承振动信号,然后用两种方法进行特征信号分析:一是对信号进行经验模式分解,仅提取第一模态函数做Hilbert变换,从而根据包络图判断轴承的早期故障;二是对信号进行预处理,滤去与轴承故障无关的主要分量,再采用小波滤波器提取微弱的故障信号,进而根据故障指标的数值来判断。最后对这两种方法对比研究,试验结果表明,方法2可以更加有效地诊断出早期故障,具有一定的工程应用价值。

The conditon monitoring and incipient fault diagnosis of bearing of double fed induction generation （DFIG） can detect the fault timely and reduce wind farm＇s operation and maintenance cost effectively. Collec- ting the bearing signal through vibration acceleration sensor, and then the signal analysis can use the following two ways. One is the empirical mode decomposition （EMD） which is used to extract the first intrinsic mode function for its Hilbert transform, and then determine the incipient bearing failure according to the change of envelope graph. The other is to filter the main irrelative components of bearing fault, and then extract the weak signal by wavelet filter, thus it can determine bearing fault according to the value of fault index. Finally, comparing the two methods, the experimental results show that the second method is a more effective way to diagnosis the incipient bearing failure of DFIG,and it has some value in engineering.