Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN

Wang Jinrui; Xie Lirong; Wang Zhongqiang; Niu Yongchao

doi:10.16578/j.issn.1004.2539.2018.03.029

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Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN

更新时间：2022-10-20

- Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN
- Vol. 42, Issue 3, Pages: 139-143(2018)
- 作者机构：
  
  1. 新疆大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.03.029
  CLC：
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[1]王晋瑞,谢丽蓉,王忠强,牛永朝.基于MEEMD-DHENN的滚动轴承故障诊断[J].机械传动,2018,42(03):139-143.

Wang Jinrui, Xie Lirong, Wang Zhongqiang, et al. Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN. [J]. 42(3):139-143(2018)
[1]王晋瑞,谢丽蓉,王忠强,牛永朝.基于MEEMD-DHENN的滚动轴承故障诊断[J].机械传动,2018,42(03):139-143. DOI： 10.16578/j.issn.1004.2539.2018.03.029.

Wang Jinrui, Xie Lirong, Wang Zhongqiang, et al. Fault Diagnosis of Rolling Bearing based on MEEMD-DHENN. [J]. 42(3):139-143(2018) DOI： 10.16578/j.issn.1004.2539.2018.03.029.

摘要

针对滚动轴承故障信号非平稳、非线性的特点,提出了把改进的经验模态分解（Modified Ensemble EMD,MEEMD）与双隐层Elman神经网络（Double Hidden-layer Elman Neural Network,DHENN）相结合识别故障类型的方法。用MEEMD选出主要本征模态函数（Principal Intrinsic Mode Function,PIMF）,先计算出经补充的总体平均经验模态分解（Complementary Ensemble Empirical Mode Decomposition,CEEMD）得到的各IMF的排列熵值,剔除虚假分量;再对其余分量进行重构用经验模态分解（Empirical mode decomposition,EMD）,计算各IMF的方差贡献率,排除贡献率最小的,即为PIMF,经希尔伯特变换求出其能量熵值,构成特征矩阵。构建了双隐层Elman神经网络对故障类型进行识别,通过经验公式与实验得到最佳隐层节点组合,最后与EMD-DHENN和MEEMD-ENN的测试结果相对比,表明基于MEEMD-DHENN识别方法只需迭代26步即可对轴承故障准确分类。

Abstract

According to the non-stationary and nonlinearity of the fault signals from rolling bearing,a recognition method based on Modified Ensemble Empirical Mode Decomposition（ MEEMD） and Double Hiddenlayer Elman Neural Network（ DHENN） is proposed. The vibration signal is decomposed by MEEMD,and using variance contribution to select out the Principal Intrinsic Mode Function（ PIMF）,one can calculate the permutation entropy of the IMF which obtained by using Complementary Ensemble Empirical Mode Decomposition（ CEEMD）,it is basic to deal with the illusive component,and the others are reconstructed and decomposed by using EMD,the variance contribution is calculated,through Hilbert transform,characteristic matrix is constructed. The DHENN network model is constructed to recognize the fault type,getting the best combine of the double hidden neural network. Finally,compare EMD-DHENN and MEEMD-ENN,the result show that the proposed MEEMD-DHENN method has a high accuracy rate of 100% and only need 26 steps.

关键词

滚动轴承故障诊断改进的经验模态分解双隐层Elman神经网络

Keywords

Rolling bearingFault diagnosisMEEMDDHENN

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