基于随机共振和VMD分解的风电机组滚动轴承故障特征提取

以风电机组滚动轴承为研究对象,针对其故障诊断中强噪声背景下信号信噪比低、故障特征难以提取的问题,提出一种基于随机共振(SR)和变分模态分解(VMD)的故障特征提取方法。该方法首先利用随机共振对滚动轴承的振动信号进行降噪处理,提高信号的信噪比;然后对降噪后的振动信号进行VMD分解,通过求取固有模态函数(IMF)的幅值谱,从而发现滚动轴承的故障特征频率。将该方法应用于风电机组滚动轴承的实际数据中,分析结果表明,该方法能够提高信号的信噪比,实现风电机组滚动轴承的精确诊断。

Fault Feature Extraction of Wind Turbines'Rolling Bearing Based on Stochastic Resonance and VMD

Taking the rolling bearing of wind turbine as the research object, and aiming at the problem that the signal-to-noise ratio is low and the fault feature is difficult to be extracted under the background of strong noise, the paper proposes a new fault feature extraction method based on stochastic resonance (SR) and variational mode deconposition (VMD). The method uses stochastic resonance to treat the vibration signal of the rolling bearing, and the signal-to-noise ratio of the signal is improved.Then,the vibration signal after noise reduction is decomposed by VMD,and the failure frequency of the rolling bearing is obtained by calculating the amplitude spectrum of the intrinsic modal function(IMF).The method is applied to the actual data of the rolling bearing of the wind turbine.The analysis results show that the method can improve the signal-to-noise ratio of the signal and realize the accurate diagnosis of the rolling bearing of the wind turbine.