基于VMD和双谱的风电机组滚动轴承故障特征提取

The Extraction of Rolling Bearing Fault Features Based on VMD and Bispectrum

针对背景噪声下风机滚动轴承故障特征提取的问题,提出了基于变分模态分解(Variational Mode Decomposition,VMD)和双谱的故障特征提取方法。运用VMD方法对信号进行自适应分解,根据峭度-相关系数准则筛选出故障特征明显的2个分量并进行信号重构;对重构信号进行双谱估计分析,仿真结果表明具有良好的噪声抑制能力;根据双谱图分析结果,提取出滚动轴承的故障特征,通过故障仿真验证了所提方法的有效性。将该方法应用于风机滚动轴承故障信号的故障特征提取,可以有效地识别出滚动轴承不同的故障特征,从而准确诊断出滚动轴承存在的故障。

Aiming at extracting wind turbine rolling bearing fault feature against the background noise,the method based on variational mode decomposition and bispectrum was proposed. Firstly,the rolling bearing fault signal was decomposed using VMD. The two components,which had obvious impact features,were extracted and reconstructed using the kurtosis-correlation coefficient criteria. Secondly,the reconstructed signal was analyzed using the bispectrum. The method has good noise suppression capability. Lastly,according to the bispectrum analysis,the fault feature of rolling bearing could be extracted. The analysis of rolling bearing fault simulation signal verifies the effectiveness of the proposed method. And it was applied to extract the fault features of the bearing fault test signal. The different fault features of rolling bearing could be identified effectively. Thus the fault diagnosis can be achieved accurately.