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基于改进 EEMD 和谱峭度的滚动轴承故障诊断

  

  1. 石家庄铁道大学电气与电子工程学院，河北 石家庄 050043
  • 出版日期:2017-10-31 发布日期:2017-11-03
  • 基金资助:
    国家自然科学基金项目(11372199，51405313，11572206)；河北省自然科学基金项目(A2014210142)

Rolling Bearing Fault Diagnosis Based on Improved EEMD and Spectrum Kurtosis

  1. Electrical and Electronics Engineering, Shijiazhuang Railway University, Shijiazhuang Hebei 050043, China
  • Online:2017-10-31 Published:2017-11-03

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摘要： 针对滚动轴承早期故障振动信号受噪声影响、总体经验模态分解(EEMD)参数不易
获取的问题，提出了基于改进 EEMD 和谱峭度的滚动轴承故障诊断方法。首先提取信号高频成
分及设置期望分解误差确定 EEMD 参数，利用 EEMD 将信号分解为若干个本征模态分量(IMF)，
依据峭度准则选取相应分量进行重构以突出故障信息、提高信噪比；然后利用快速谱峭度图来
选取带通滤波器的参数；最后对滤波信号进行能量算子解调分析。该方法应用到实测数据中的
结果表明，其不仅能够自适应确定 EEMD 参数，降低了噪声的影响，还能清晰、准确地提取出
故障特征频率，实现了滚动轴承故障的精确诊断。

关键词: 滚动轴承, 经验模态分解, 谱峭度, 故障诊断

Abstract: In order to solve the problems that the early fault vibration signals of rolling bearing
affected by the noise, the parameters of ensemble empirical mode decomposition (EEMD) are not
easy to be obtained. A method for fault bearings of rolling bearings based on improved EEMD and
spectrum kurtosis (SK) was presented. Firstly, obtaining the amplitude coefficient of added noise by
extracting the high frequency information, the number of ensemble members is obtained by
calculating the expectation error, the fault signal was decomposed into several intrinsic mode function
(IMF) by improved EEMD, the IMFs were reconstructed based on kurtosis criterion. Then, the central
frequency and bandwidth of a band-pass filter were determined with spectral kurtosis. Last, the
filtered signal was analyzed by using energy operator demodulation spectrum. The results
demonstrate that the proposed method can not only solve the problems such as losing fault
information and leaving noise due to the mode mixing in the process of denoising, but also extract the
fault frequency accurately.

Key words: rolling bearing, ensemble empirical mode decomposition, spectrum kurtosis, fault diagnosis