基于经验模式分解的全息谱故障识别方法
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    摘要:

    由于实测信号中的一些噪声干扰会影响全息谱对信号分析的准确性,采用经验模式分解( empirical mode decomposition,简称EMD)方法进行信号滤波以提高识别的可靠性。应用EMD对 信号分解,并结合互相关系数对内蕴模式分量(intrinsic mode function,简称IMF)进行滤波 ,在此基础上对信号进行重构,以降低噪声干扰,并对实际测试信号进行有 效提纯。最后,对滤波后的转子信号进行全息谱分析,并通过分析实际转子碰摩信号 来验证该方法的有效性。

    Abstract:

    Holospectrum is widely used to rotor condition classification. The acc uracy is usually affected by the noise interference from measured signals. Accor ding to the practical problem for rotor signal analysis, empirical mode decompos ition (EMD) was applied to signal filter to improve reliability for condition cl assification. Firstly, EMD was used for signal decomposition and intrinsic mode function (IMF) was obtained. Then, in order to suppress the noise interference, the IMF data were filtered by using the cross correlation function and the signa ls were reconstructed. Lastly, the reconstructed signal was employed for holospe ctrum analysis. A rotor rubbing fault was studied as an example to testify the e ffectiveness of the method.

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  • 收稿日期:2009-09-25
  • 最后修改日期:2010-04-23
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