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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

Figures

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Fig. 3

Flowchart of multimode search algorithm

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Fig. 2

The framework of the optimal EEMD method

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Fig. 1

The decomposition results of EEMD including the intermittent signal

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

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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. 6

The decomposition results of the traditional EEMD

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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. 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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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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The HHT spectrum of defective bearing 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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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. 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. 27

Results of acceleration 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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