A study on the method for eliminating mode mixing in B-spline empirical mode decomposition based on adaptive bandwidth constrained signal

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (16) : 83-90.

JIANG Yonghua，JIAO Weidong，LI Rongqiang，TANG Chao，ZHENG Jiajia，CAI Jiancheng

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Abstract

Mode mixing is an inevitable problem in empirical mode decomposition (EMD) which significantly impacts its engineering application.The main reasons of causing mode mixing were summarized and the mechanism of mode mixing was analyzed.Inspired by high frequency harmonic added EMD (HFHA-EMD), an improved method for eliminating mode mixing in B-spline Empirical Mode Decomposition (BS-EMD) based on adaptive bandwidth constrained signal was proposed.According to the first IMF obtained by BS-EMD of the analyzed signal, the frequency and amplitude of the bandwidth constrained signal were determined.Thus, the adaptive bandwidth constrained signal was constructed.The central frequency of the EMD band pass filter was changed by adding the adaptive bandwidth constrained signal to the original signal.And then BS-EMD was performed to eliminate the mode mixing.Compared with EMD and BS-EMD, the effectiveness and feasibility of the proposed method was verified.Compared with HFHA-EMD, simulation results show that both two methods can avoid mode mixing correctly, but the proposed method was more explicit and feasible to construct the adaptive bandwidth constrained signal, and more adaptive.An actual fault rotor signal with complex abnormal events was also analyzed, and the effectiveness and feasibility in engineering were verified.

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empirical mode decomposition (EMD) / mode mixing / Adaptive Bandwidth Constraint / B-spline

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JIANG Yonghua，JIAO Weidong，LI Rongqiang，TANG Chao，ZHENG Jiajia，CAI Jiancheng. A study on the method for eliminating mode mixing in B-spline empirical mode decomposition based on adaptive bandwidth constrained signal[J]. Journal of Vibration and Shock, 2018, 37(16): 83-90

References

[1] 曹莹, 段玉波, 刘继承. Hilbert-Huang变换中的模态混叠问题[J]. 振动、测试与诊断, 2016, 36(3): 518-523.
   CAO Y, DUAN Y B, LIU J C. Research and application of mode-mixing in Hilbert-Huang transform [J]. Journal of Vibration, Measurement & Diagnosis, 2016, 36(3): 518-523.
[2] HUANG N E，SHEN Z，LONG S R. The empirical mode
decomposition and the Hilbert spectrum for nonlinear and
non-stationary time series analysis [J]. Proceeding of Royal Society London A, 1998, 454: 903-995.
[3] HUANG N E，SHEN Z，LONG S R. A new view of nonlinear
water waves：The Hilbert spectrum [J]. Annual Review of Fluid Mechanics, 1999, 31(3): 417-457.
[4] 胡爱军, 孙敬敬, 向玲. 经验模态分解中的模态混叠问题[J]. 振动、测试与诊断, 2011, 31(4): 429-434.
HU A J, SUN J J, XIANG L. Mode mixing in empirical mode decomposition [J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4): 429-434.
[5] 汤宝平, 蒋永华, 张详春. 基于形态奇异值分解和经验模态分解的滚动轴承故障特征提取方法[J]. 机械工程学报, 2010, 46( 5): 37-42, 48.
TANG B P, JIANG Y H, ZHANG X CH. Feature extraction method of rolling bearing fault based on singular value decomposition-morphology filter and empirical mode decomposition [J]. Chinese Journal of Mechanical Engineering, 2010, 46(5): 37-42, 48.
[6] 赵玲, 刘小峰, 秦树人, 等. 消除经验模态分解中混叠现象的改进掩膜信号法[J]. 振动与冲击, 2010, 29(9): 13-17.
   ZHAO L, LIU X F, QIN S R, et al. Use of masking signal to improve empirical mode decomposition [J]. Journal of Vibration and Shock, 2010, 29(9): 13-17.
[7] 马文朋, 张俊红,马梁, 等. 改进的经验模式分解在机械故障诊断中的应用 [J]. 振动、测试与诊断, 2015, 35(4): 637-644.
   MA W P, ZHANG J H, MA L, et al. Application of improved emprical mode decomposition in machinery fault diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2015, 35(4): 637-644.
[8] RILLING G, FLANDRIN P, GONE P, et a1. On empirical mode decomposition and its algorithm [C]. IEEE-EURASIP Work-shop on Nonlinear Signal and Image Processing, NSIP-03, 2003, 1-5．
[9] 郑源, 潘天航, 王辉斌, 等. 改进EMD-ICA去噪在水轮机组隐蔽碰磨诊断中的应用研究 [J]. 振动与冲击, 2017, 36(6): 235-240.
   ZHENG Y, PAN T H, WANG H B, et al. Improved EMD-ICA method used in the hidden rubbing fault diagnosis of turbine units [J]. Journal of Vibration and Shock, 2017, 36(6): 235-240.
[10] 禹丹江, 任伟新. 信号经验模式分解与间断频率[J]. 福州大学学报(自然科学版), 2005, 33(5): 638-642.
YU D J, REN W X. Signal empirical mode decomposition and intermittency frequency [J]. Journal of Fuzhou University (Natural Science), 2005, 33(5): 638-642.
[11] RYAN D, JAMES F K. The use of a masking signal to improve empirical mode decomposition [C]. International Conference on Acoustics, Speed, and Signal Processing (ICASSP), IEEE, 2005:485-488.
[12] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[13] 肖瑛, 殷福亮. 解相关EMD：消除模态混叠的新方法 [J]. 振动与冲击, 2015, 34(4): 25-29.
XIAO Y, YIN F L. Decorrelation EMD: a new method of eliminating mode mixing [J]. Journal of Vibration and Shock, 2015, 34(4): 25-29.
[14] 陈建国, 张志新, 郭正刚, 等. 独立分量分析方法在经验模态分解中的应用 [J]. 振动与冲击, 2009, 28(1): 109-111.
   CHEN J G, ZHANG Z X, GUO Z G, et al. Application of independent component analysis in empirical mode decomposition [J]. Journal of Vibration and Shock, 2009, 28(1): 109-111.
[15] 汤宝平, 董绍江, 马靖华. 基于独立分量分析的EMD模态混叠消除新方法 [J]. 仪器仪表学报, 2012, 33(7): 1477-1482.
TANG B P, DONG S J, MA J H. Study on the method for eliminating mode mixing of empirical mode decomposition based on independent component analysis [J]. Chinese Journal of Scientific Instrument, 2012, 33(7): 1477-1482.
[16] 郑近德, 程军圣. 改进希尔伯特—黄变换及其在滚动轴承故障诊断中的应用 [J]. 机械工程学报, 2015, 51(1): 138-145.
   ZHENG J D, CHENG J S. Improved Hilbert-Huang transform and its applications to rolling bearing fault diagnosis [J].Journal of Mechanical engineering, 2015, 51(1): 138-145.