利用FDM和随机共振检测风机主轴承微弱旋转信号

Weak Rotating Signal Detection of Wind Turbine Main Bearing Using FDM and Stochastic Resonance

轴承作为风力发电机设备中重要部件,其健康状态直接影响风力发电机运行的稳定性和现场的安全可靠性。由于风力发电机特殊的工作环境,导致采集到的振动信号中包含大量的噪声干扰,难以准确提取轴承振动信号包含的信息成分,给评估主轴承健康状态带来困难。因此本文采用将傅立叶分解(Fourier decomposition method, FDM)和随机共振(Stochastic resonance, SR)相结合的方式提取信号中微弱的轴承振动信息。首先用FDM将原始信号自适应地分解为一系列包含轴承振动特征的傅立叶频带函数,然后找出相关性大的频带函数进行重构,最后采用SR对重构信号进行分析获得特征频率,判断轴承的健康状态。结果显示,将两种方法相结合能有效提高输出信噪比,提升特征频率检测的精度,为实现风机轴承早期微弱故障诊断提供帮助。

Bearings are an important component of wind turbines,and their health directly affects the stability of wind turbine operation and the reliability of the work site.Due to the special working environment of wind turbines,the collected vibration signals contain a large amount of noise interference,it is difficult to accurately extract the information contained in the bearing vibration signals,and also hard to assess the health status of the main bearing.Therefore,in this paper,the Fourier decomposition method(FDM)and Stochastic resonance(SR)are combined to extract the weak bearing vibration information in the signals.Firstly,FDM is used to adaptively decompose the original signals into a series of Fourier intrinsic band functions(FIBFS),which containing bearing vibration characteristics,then find the FIBFS with large correlation for reconstruction,and finally use SR to analyze the reconstructed signals to obtain the characteristic frequency.The results show that the combination of the two methods can effectively improve the output signal to noise ratio,improve the accuracy of the characteristic frequency detection,and provide help for the early diagnosis of weak faults in wind turbine bearings.