一种新的内禀模态函数判据

摘要

针对现有EMD(Empirical Mode Decomposition)分解过程中内禀模态函数判据必须确定某一经验值的缺点,本文提出了一种基于信息熵的内禀模态函数判据,该判据不必设定某一参数的经验值,从而避免了由于经验取值不同造成分解结果有很大差异的缺陷.通过与现有方法的对比表明:依此判据所得内禀模态函数更准确,能够很好的体现信号的非线性、非平稳特征,同时使分解结果具有更好的正交性.将此判据应用在风力发电机组齿轮箱高速端轴承的故障诊断中,验证了依此判据所得分解结果更好的保留了冲击脉冲、幅值和频率调制等故障特征信息,从而准确诊断出故障部位所在.%In order to solve the disadvantage that it needs to set an experiential value of existing stop criterion of Intrinsic Mode Functionin EMD (Empirical Mode Decomposition) sifting process, the paper proposes a new criterion of Intrinsic Mode Function based on Shannon entropy. There is no need to set an experiential value of a parameter, which avoids the defects of differences in decomposition results caused by different experience value in sifting process. By comparing with other criterion, it demonstrates that the decomposition result are more accurate and have smaller index of orthogonality according to the proposed stop criterion, the IMFs can reflect the characteristic of non-stationary and nonlinear in signal. This stop criterion is applied to the fault diagnosis of rolling bearing in wind turbine. The result showes that this criterion can better retain the fault feature information such as the shock pulse, amplitude and frequency modulation, and diagnose the fault site accurately.