Median Complementary Ensemble Empirical Mode Decomposition and its application to time-frequency analysis of industrial oscillations

被引：0
作者：
Liu, Songhua [1 ]
He, Bingbing [1 ]
Chen, Qiming [2 ]
Lang, Xun [1 ]
Zhang, Yufeng [1 ]
机构：
[1] Yunnan Univ, Informat Sch, Dept Elect Engn, Kunming 650504, Yunnan, Peoples R China
[2] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
来源：
2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Mode splitting; median operator; mean operator; industrial oscillation; time-frequency analysis;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Median ensemble empirical mode decomposition (MEEMD) represents a remarkable improvement based on the ensemble empirical mode decomposition (EEMD) method for alleviating the mode splitting and mode mixing problem. However, the single use of the median operator generates tough problems including the higher reconstruction error, and the presence of burr in decomposition products. Aiming at addressing these problems while catering to a better time-frequency representation of the industrial oscillations, a median complementary EEMD (MCEEMD) method is proposed in this paper. In this work, the median operator and the mean operator are skillfully combined during the ensemble process. Through the study on simulation and typical industrial oscillation case, the effectiveness of MCEEMD is verified compared with existing methods, including EEMD, CEEMD and MEEMD.
引用
收藏
页码：2999 / 3004
页数：6
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