An improved complementary ensemble empirical mode decomposition method and its application in rolling bearing fault diagnosis

被引:69
作者
Gu, Jun [1 ]
Peng, Yuxing [1 ,2 ]
机构
[1] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Peoples R China
[2] China Univ Min & Technol, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou 221116, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Fault diagnosis; CEEMD; Intrinsic mode function; Permutation entropy; Envelope spectrum; PERMUTATION ENTROPY; HILBERT SPECTRUM; NOISE;
D O I
10.1016/j.dsp.2021.103050
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Although ensemble empirical mode decomposition (EEMD) can suppress the modal confusion phenomenon in the EMD method to a certain extent, the added white noise cannot be completely neutralized. The complementary EEMD (CEEMD) adds white noise with opposite signs to the analysis signal in pairs, which greatly reduces the reconstruction error. Aiming at the problems of modal confusion and the difficulty in accurately extracting fault features of rolling bearings, a CPCEEMD (CEEMD-PE-CEEMD) bearing fault diagnosis method is proposed, which fully combines the CEEMD algorithm and the advantages of signal randomness detection based on permutation entropy (PE). After the abnormal components of the CEEMD are detected by permutation entropy, CEEMD of the remaining signals is conducted directly. Intrinsic mode function (IMF) components with large correlation coefficients are selected for Hilbert envelope spectrum analysis, and fault features are extracted from the envelope diagram. By analyzing the simulation signal and the measured bearing signal, the results show that the proposed method has a good decomposition effect, results in a certain inhibition effect on the modal confusion in the EMD process, and can effectively extract the characteristic information of the rolling bearing fault signal, which is feasible. (C) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页数:10
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