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Research Papers

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

[+] Author and Article Information
Shi-Chang Du

State Key Lab of Mechanical
System and Vibration;
Department of Industrial
Engineering and Management,
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: lovbin@sjtu.edu.cn

Tao Liu

Department of Industrial
Engineering and Management,
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: l_yz2007@163.com

De-Lin Huang

Department of Industrial
Engineering and Management,
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: cjwanan@sjtu.edu.cn

Gui-Long Li

Department of Industrial
Engineering and Management,
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: lgllg68629315@qq.com

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received August 24, 2016; final manuscript received November 29, 2016; published online March 16, 2017. Assoc. Editor: Mohammed Daqaq.

J. Vib. Acoust 139(3), 031003 (Mar 16, 2017) (18 pages) Paper No: VIB-16-1416; doi: 10.1115/1.4035480 History: Received August 24, 2016; Revised November 29, 2016

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

Copyright © 2017 by ASME
Topics: Bearings , Vibration , Signals
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References

Feng, Z. , Liang, M. , and Cu, F. , 2013, “ Recent Advances in Time-Frequency Analysis Methods for Machinery Fault Diagnosis: A Review With Application Examples,” Mech. Syst. Signal Process., 38(1), pp. 165–205. [CrossRef]
Roth, J. T. , Djurdjanovic, D. , Yang, X. , Mears, L. , and Kurfess, T. , 2010, “ Quality and Inspection of Machining Operations: Tool Condition Monitoring,” ASME J. Manuf. Sci. Eng., 132(4), p. 041015. [CrossRef]
Du, S. , Lv, J. , and Xi, L. , 2012, “ Degradation Process Prediction for Rotational Machinery Based on Hybrid Intelligent Model,” Rob. Comput. Integr. Manuf., 28(2), pp. 190–207. [CrossRef]
Lee, J. , Wu, F. J. , Zhao, W. Y. , Ghaffari, M. , Liao, L. X. , and Siegel, D. , 2014, “ Prognostics and Health Management Design for Rotary Machinery Systems—Reviews, Methodology and Applications,” Mech. Syst. Signal Process., 42(1–2), pp. 314–334. [CrossRef]
Yang, Y. , Dong, X. J. , Peng, Z. K. , Zhang, W. M. , and Meng, G. , 2015, “ Vibration Signal Analysis Using Parameterized Time-Frequency Method for Features Extraction of Varying-Speed Rotary Machinery,” J. Sound Vib., 335(5), pp. 350–366. [CrossRef]
Luo, J. S. , Yu, D. J. , and Liang, M. , 2012, “ Application of Multi-Scale Chirplet Path Pursuit and Fractional Fourier Transform for Gear Fault Detection in Speed Up and Speed-Down Processes,” J. Sound Vib., 331(22), pp. 4971–4986. [CrossRef]
Lin, J. , and Qu, L. S. , 2000, “ Feature Extraction Based on Morlet Wavelet and Its Application for Mechanical Fault Diagnosis,” J. Sound Vib., 234(1), pp. 135–148. [CrossRef]
Yang, Y. , Zhang, W. M. , Peng, Z. K. , and Meng, G. , 2013, “ Multicomponent Signal Analysis Based on Polynomial Chirplet Transform,” IEEE Trans. Ind. Electron., 60(9), pp. 3948–3956. [CrossRef]
Peng, Z. , Chu, F. , and He, Y. , 2002, “ Vibration Signal Analysis and Feature Extraction Based on Reassigned Wavelet Scalogram,” J. Sound Vib., 253(5), pp. 1087–1100. [CrossRef]
Peng, F. Q. , Yu, D. J. , and Luo, J. S. , 2011, “ Sparse Signal Decomposition Method Based on Multi-Scale Chirplet and Its Application to the Fault Diagnosis of Gearboxes,” Mech. Syst. Signal Process., 25(2), pp. 549–557. [CrossRef]
Xu, C. , Wang, C. , and Liu, W. , 2016, “ Nonstationary Vibration Signal Analysis Using Wavelet-Based Time–Frequency Filter and Wigner–Ville Distribution,” ASME J. Vib. Acoust., 138(5), p. 051009. [CrossRef]
Peng, Z. K. , and Chu, F. L. , 2004, “ Application of the Wavelet Transform in Machine Condition Monitoring and Fault Diagnostics: A Review With Bibliography,” Mech. Syst. Signal Process., 18(2), pp. 199–221. [CrossRef]
Peng, Z. K. , Tse, P. W. , and Chu, F. L. , 2005, “ An Improved Hilbert–Huang Transform and Its Application in Vibration Signal Analysis,” J. Sound Vib., 286(1–2), pp. 187–205. [CrossRef]
Yang, Y. , Yu, D. , and Cheng, J. , 2006, “ A Roller Bearing Fault Diagnosis Method Based on EMD Energy Entropy and ANN,” J. Sound Vib., 294(1–2), pp. 269–277.
Hong, S. , Wang, B. , Li, G. , and Hong, Q. , 2014, “ Performance Degradation Assessment for Bearing Based on Ensemble Empirical Mode Decomposition and Gaussian Mixture Model,” ASME J. Vib. Acoust., 136(6), p. 061006. [CrossRef]
Huang, N. E. , Shen, Z. , Long, S. R. , Wu, M. L. , Shih, H. H. , Zheng, Q. N. , Yen, N. C. , Tung, C. C. , and Liu, H. H. , 1998, “ The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis,” Proc.: Math., Phys. Eng. Sci., 454(1971), pp. 903–995. [CrossRef]
Sun, Y. , Zhuang, C. , and Xiong, Z. , 2015, “ Transform Operator Pair Assisted Hilbert–Huang Transform for Signals With Instantaneous Frequency Intersections,” ASME J. Vib. Acoust., 137(6), p. 061016. [CrossRef]
Wang, K. S. , and Heyns, P. S. , 2011, “ Application of Computed Order Tracking, Vold–Kalman Filtering and EMD in Rotating Machine Vibration,” Mech. Syst. Signal Process., 25(1), pp. 416–430. [CrossRef]
Lei, Y. , Lin, J. , He, Z. , and Zuo, M. J. , 2013, “ A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery,” Mech. Syst. Signal Process., 35(1–2), pp. 108–126. [CrossRef]
Huang, N. E. , Wu, M. L. C. , Long, S. R. , Shen, S. S. P. , Qu, W. D. , Gloersen, P. , and Fan, K. L. , 2003, “ A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis,” Proc. R. Soc. London, A, 459(2037), pp. 2317–2345. [CrossRef]
Deering, R. , and Kaiser, J. E. , 2005, “ The Use of a Masking Signal to Improve Empirical Mode Decomposition,” IEEE International Conference on Acoustics, Speech, and Signal Processing, 1–5: Speech Processing, Mar. 23, pp. 485–488.
Grasso, M. , and Colosimo, B. M. , 2016, “ An Automated Approach to Enhance Multiscale Signal Monitoring of Manufacturing Processes,” ASME J. Manuf. Sci. Eng., 138(5), p. 051003. [CrossRef]
Wu, Z. , and Huang, N. E. , 2009, “ Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method,” Adv. Adapt. Data Anal., Theory Appl., 1(01), pp. 1–41. [CrossRef]
Peng, Y. , 2006, “ Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage,” ASME J. Manuf. Sci. Eng., 128(1), pp. 154–166. [CrossRef]
Guo, W. , and Tse, P. W. , 2013, “ A Novel Signal Compression Method Based on Optimal Ensemble Empirical Mode Decomposition for Bearing Vibration Signals,” J. Sound Vib., 332(2), pp. 423–441. [CrossRef]
Guo, W. , and Tse, P. W. , 2010, “ Enhancing the Ability of Ensemble Empirical Mode Decomposition in Machine Fault Diagnosis,” Prognostics & Health Management Conference, Jan. 12–14.
Amarnath, M. , and Krishna, I. R. P. , 2013, “ Detection and Diagnosis of Surface Wear Failure in a Spur Geared System Using EEMD Based Vibration Signal Analysis,” Tribol. Int., 61, pp. 224–234. [CrossRef]
Feng, Z. P. , Liang, M. , Zhang, Y. , and Hou, S. M. , 2012, “ Fault Diagnosis for Wind Turbine Planetary Gearboxes Via Demodulation Analysis Based on Ensemble Empirical Mode Decomposition and Energy Separation,” Renewable Energy, 47, pp. 112–126. [CrossRef]
Caesarendra, W. , Kosasih, P. B. , Tieu, A. K. , Moodie, C. A. S. , and Choi, B. K. , 2013, “ Condition Monitoring of Naturally Damaged Slow Speed Slewing Bearing Based on Ensemble Empirical Mode Decomposition,” J. Mech. Sci. Technol., 27(8), pp. 2253–2262. [CrossRef]
Tabrizi, A. , Garibaldi, L. , Fasana, A. , and Marchesiello, S. , 2015, “ Early Damage Detection of Roller Bearings Using Wavelet Packet Decomposition, Ensemble Empirical Mode Decomposition and Support Vector Machine,” Meccanica, 50(3), pp. 865–874. [CrossRef]
Georgoulas, G. , Tsoumas, I. P. , Antonino-Daviu, J. A. , Climente-Alarcon, V. , Stylios, C. D. , Mitronikas, E. D. , and Safacas, A. N. , 2014, “ Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines,” IEEE Trans. Ind. Electron., 61(9), pp. 4937–4946. [CrossRef]
Yan, R. , and Gao, R. X. , 2008, “ Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition,” ASME J. Vib. Acoust., 130(2), p. 021007. [CrossRef]
Feng, Z. , Zuo, M. J. , Hao, R. , Chu, F. , and Lee, J. , 2013, “ Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis,” ASME J. Vib. Acoust., 135(3), p. 031013. [CrossRef]
Chen, L. , Zi, Y. Y. , He, Z. J. , Lei, Y. G. , and Tang, G. S. , 2014, “ Rotating Machinery Fault Detection Based on Improved Ensemble Empirical Mode Decomposition,” Adv. Adapt. Data Anal., 6(2–3), p. 1450006. [CrossRef]
Zhang, J. A. , Yan, R. Q. , Gao, R. X. , and Feng, Z. H. , 2010, “ Performance Enhancement of Ensemble Empirical Mode Decomposition,” Mech. Syst. Signal Process., 24(7), pp. 2104–2123. [CrossRef]
Yeh, J.-R. , Shieh, J.-S. , and Huang, N. E. , 2010, “ Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method,” Adv. Adapt. Data Anal., 2(2), pp. 135–156. [CrossRef]
Bekka, R. E. H. , and Berrouche, Y. , 2013, “ Improvement of Ensemble Empirical Mode Decomposition by Over-Sampling,” Adv. Adapt. Data Anal., 5(3), p. 1350012. [CrossRef]
Zheng, J. D. , Cheng, J. S. , and Yang, Y. , 2014, “ Partly Ensemble Empirical Mode Decomposition: An Improved Noise-Assisted Method for Eliminating Mode Mixing,” Signal Process., 96, pp. 362–374. [CrossRef]
Xue, X. M. , Zhou, J. Z. , Xu, Y. H. , Zhu, W. L. , and Li, C. S. , 2015, “ An Adaptively Fast Ensemble Empirical Mode Decomposition Method and Its Applications to Rolling Element Bearing Fault Diagnosis,” Mech. Syst. Signal Process., 62–63, pp. 444–459. [CrossRef]
Hooke, R. , and Jeeves, T. A. , 1961, “ Direct Search Solution of Numerical and Statistical Problems,” J. ACM, 8(2), pp. 212–229. [CrossRef]
CWRU, 2015, “ Bearing Data Center,” Case Western University, Cleveland, OH.
NASAARC, 2015, “ IMS Bearings Data Set,” NASA Ames Research Center, Moffett Field, CA.

Figures

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Fig. 1

The decomposition results of EEMD including the intermittent signal

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Fig. 2

The framework of the optimal EEMD method

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Fig. 3

Flowchart of multimode search algorithm

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Fig. 4

Axial search mode and pattern search mode in two-dimensional coordinate system

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Fig. 5

The simulated signal and its components

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Fig. 6

The decomposition results of the traditional EEMD

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Fig. 7

The decomposition results of the improved EEMD

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Fig. 8

The decomposition results of the optimal EEMD

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Fig. 9

Comparison between reconstructed signal using three methods with the original signal

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Fig. 10

Relationship between the value of index of orthogonality IO and the amplitude ratio of white noise α

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Fig. 11

Result of vibration signals from normal bearings decomposed by the optimal EEMD

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Fig. 12

The HHT spectrum of normal bearing signals decomposed by the optimal EEMD

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Fig. 13

The frequency spectrum of normal bearing signals decomposed by the optimal EEMD

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Fig. 14

Results of vibration signals from defective bearing decomposed by the optimal EEMD

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Fig. 15

The HHT spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 16

The frequency spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 17

The envelope spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 18

Result of vibration signal from normal bearings decomposed by the optimal EEMD

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Fig. 19

The HHT spectrum of normal bearing signals decomposed by the optimal EEMD

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Fig. 20

The frequency spectrum of normal bearing signals decomposed by the optimal EEMD

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Fig. 21

Result of vibration signals from defective bearing decomposed by the optimal EEMD

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Fig. 22

The HHT spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 23

The frequency spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 24

The envelope spectrum of defective bearing signals decomposed by the optimal EEMD

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Fig. 25

The experimental setup

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Fig. 26

The original acceleration signal of one running sample

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Fig. 27

Results of acceleration signals decomposed by the optimal EEMD

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Fig. 28

The HHT spectrum of acceleration signals decomposed by the optimal EEMD

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Fig. 29

The frequency spectrum of acceleration signals decomposed by the optimal EEMD

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Fig. 30

The envelope spectrum of acceleration signals decomposed by the optimal EEMD

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