Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis

doi:10.3901/JME.2018.23.093

Abstract

Abstract: Empirical mode decomposition (EMD), as an adaptive signal decomposition method, has been widely applied to many engineering fields. Two kinds of mean optimized empirical mode decomposition (MOEMD) methods are developed to improve the decomposition performance of EMD. The first method is based on selecting decomposition results with the smallest orthogonality that obtained by different weighted mean curve iteration sifting as the optimal one and the second is selecting the optimal intrinsic mode function in each layer from the results obtained by different weighted mean curve iteration sifting to ensure an overall optimal decomposition result. The two MOEMD algorithms are compared with EMD and other existing signal decomposing methods by analyzing simulation signals and the comparison results indicate that the proposed methods have much more significantly improvement in decomposition performance and precision than EMD and other methods. Finally, the proposed methods are applied to the rotor rubbing fault signal analysis by comparing with EMD and the results show that MOEMD method could effectively identify the rotor rubbing fault and get much better recognition effect than EMD.

Key words: empirical mode decomposition, ensemble empirical mode decomposition, fault diagnosis, local characteristic-scale decomposition, rotor rubbing

CLC Number:

TN911

ZHENG Jinde, PAN Haiyang, CHENG Junsheng. Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis[J]. Journal of Mechanical Engineering, 2018, 54(23): 93-101.

References

[1] 程军圣, 于德介,杨宇. EMD方法在转子局部碰摩故障诊断中的应用[J]. 振动、测试与诊断,2006,26(1):24-27. CHENG Junsheng,YU Dejie,YANG Yu. Application of EMD to local rub-impact fault diagnosis in rotor systems[J]. Journal of Vibration Measurement & Diagnosis,2006,26(1):24-27.
[2] HUANG N E,SHEN Z,LONG S R,et. al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series[J]. Proceedings of the Royal Society A,1998,454(1971):903-995.
[3] COLOMINAS M A,SCHLOTTHAUER G,TORRES M E. Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing & Control,2014,14(1):19-29.
[4] LEI Yaguo,LIN Jing,HE Zhengjia,et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems & Signal Processing,2013,35(1-2):108-126.
[5] 郑近德,程军圣. 改进的希尔伯特-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 Jinde,CHENG Junsheng. Improved Hilbert-Huang transform and its applications to rolling bearing fault diagnosis[J]. Journal of Mechanical Engineering,2015,51(1):138-145.
[6] WU Z,HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2011,1(1):1-41.
[7] YEH J,SHIEH J,HUANG N E. Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis,2011,2(2):135-156.
[8] ZHENG Jinde,CHENG Junsheng,YANG Yu. Partly ensemble empirical mode decomposition:an improved noise-assisted method for eliminating mode mixing[J]. Signal Processing,2014,96:362-374.
[9] 雷亚国. 基于改进Hilbert-Huang变换的机械故障诊断[J]. 机械工程学报,2011,47(5):71-77. LEI Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform[J]. Journal of Mechanical Engineering,2011,47(5):71-77.
[10] 雷亚国,孔德同,李乃鹏,等. 自适应总体平均经验模式分解及其在行星齿轮箱故障检测中的应用[J]. 机械工程学报,2014,50(3):64-70. LEI Yaguo,KONG Detong,LI Naipeng,et al. Adaptive ensemble empirical mode decomposition and its application to fault detection of planetary gearboxes[J]. Journal of Mechanical Engineering,2014,50(3):64-70.
[11] SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2006,2(5):443-454.
[12] FREI M G,OSORIO I. Intrinsic time-scale decomposition:time-frequency-energy analysis and real-time filtering of non-stationary signals[J]. Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences,2007,463(2078):321-342.
[13] ZHENG Jinde,CHENG Junsheng,YANG Yu. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy[J]. Mechanism & Machine Theory,2013,70(6):441-453.
[14] 程军圣,郑近德,杨宇. 一种新的非平稳信号分析方法-局部特征尺度分解法[J]. 振动工程学报,2012,25(2):215-220. CHENG Junsheng,ZHENG Jinde,YANG Yu. A nonstationary signal analysis approach-the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering,2012,25(2):215-220.
[15] RILLING G,FLANDRIN P,GONCALVES P. On empirical mode decomposition and its algorithms[C]//Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP 2003),Grado,Italy,June 8-11,2003.
[16] ZHENG J,CHENG J,YANG Y. Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis[J]. Mechanical Systems & Signal Processing,2013,40(1):136-153.
[17] 科恩著. 时-频分析:理论与应用[M]. 白居宪译. 西安:西安交通大学出版社,1998. COHEN L. BAI Juxian. Time frequency analysis:Theory and applications[M]. Translated by BAI Juxian. Xi'an:Xi'an Jiao Tong University Press, 1994.
[18] 程军圣. 基于Hilbert-Huang变换的旋转机械故障诊断方法研究[D]. 长沙:湖南大学,2005. CHENG Junsheng. Research on fault diagnosis of rotating machinery based on Hilbert-Huang transform[D]. Changsha:Hunan University, 2005.