电子测量与仪器学报

童靳于,苏缪涎,郑近德,潘海洋,潘紫微,包家汉.自适应噪声均值优选集成经验模态分解及其在滚动轴承故障诊断中的应用[J].电子测量与仪器学报,2021,35(2):41-49

自适应噪声均值优选集成经验模态分解及其在滚动轴承故障诊断中的应用

Mean optimized ensemble empirical mode decomposition with adaptive noise and its application in rolling bearing fault diagnosis

DOI：

中文关键词: 自适应噪声完整集成经验模态分解经验模态分解最大相关峭度反褶积滚动轴承故障诊断

英文关键词:CEEMDAN EMD maximum correlation kurtosis deconvolution rolling bearing fault diagnosis

基金项目:国家重点研发计划（2017YFC0805100）、国家自然科学基金（51975004）、安徽省自然科学基金项目（2008085QE215）、安徽省高校自然科学研究项目（KJ2019A0053，KJ2019A092，KJ2018ZD005）资助

作者	单位
童靳于	安徽工业大学马鞍山243032
苏缪涎	安徽工业大学马鞍山243032
郑近德	安徽工业大学马鞍山243032
潘海洋	安徽工业大学马鞍山243032
潘紫微	安徽工业大学马鞍山243032
包家汉	安徽工业大学马鞍山243032

Author	Institution
Tong Jinyu	Anhui University of Technology，Maanshan 243032, China
Su Miaoxian	Anhui University of Technology，Maanshan 243033, China
Zheng Jinde	Anhui University of Technology，Maanshan 243034, China
Pan Haiyang	Anhui University of Technology，Maanshan 243035, China
Pan Ziwei	Anhui University of Technology，Maanshan 243036, China
Bao Jiahan	Anhui University of Technology，Maanshan 243037, China

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中文摘要:

为了提高自适应噪声完备经验模态分解（complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN）的分解能力和分解精度，解决CEEMDAN方法中噪声残留等问题，提出了一种改进的CEEMDAN方法——自适应噪声均值优选集成经验模态分解（mean optimized ensemble empirical mode decomposition with adaptive noise，MEEMDAN）。MEEMDAN在迭代筛分过程中引入不同的权重，以正交性指标最小为依据，从不同权重下的分解结果中选取最优模态函数(IMF)，确保了每一阶的IMF分量都是整体最优。通过仿真分析验证了MEEMDAN方法在分解能力和分解精度方面优于CEEMDAN方法。同时，将MEEMDAN和最大相关峭度反褶积相结合，并应用于滚动轴承仿真数据和实测数据分析，结果表明，与现有方法相比，所提方法能够更为准确地提取出故障特征频率，且在分解能力和抑制干扰频率方面更具有优越性。

英文摘要:

In order to improve the decomposition ability and decomposition accuracy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN）, and solve the problem of noise residual in CEEMDAN, an improved CEEMDAN method called mean optimized ensemble empirical mode decomposition with adaptive noise (MEEMDAN) is proposed. MEEMDAN introduces different weights in the process of iteration screening. Based on the minimum orthogonality, the optimal IMF is selected from the decomposition results under different weights as final decomposition result to ensure that the IMFs of each order are globally optimal. The simulation results show that MEEMDAN is superior to CEEMDAN in decomposition ability and accuracy. At the same time, a new fault diagnosis method for rolling bearings combining MEEMDAN with maximum correlation kurtosis deconvolution (MCKD) is proposed and applied to the simulation and measured data analysis. The results show that, compared with the existing methods, the proposed method can extract fault characteristic frequency more accurately, and has more advantages in decomposition ability and interference suppression frequency.

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