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.

References

References
1.
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
.
2.
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
.
3.
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
.
4.
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
.
5.
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
.
6.
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
.
7.
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
.
8.
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
.
9.
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
.
10.
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
.
11.
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
.
12.
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
.
13.
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
.
14.
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
.
15.
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
.
16.
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
.
17.
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
.
18.
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
.
19.
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
.
20.
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
.
21.
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
.
22.
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
.
23.
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
.
24.
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
.
25.
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
.
26.
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.
27.
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
.
28.
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
.
29.
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
.
30.
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
.
31.
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
.
32.
Yan
,
R.
, and
Gao
,
R. X.
,
2008
, “
Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition
,”
ASME J. Vib. Acoust.
,
130
(
2
), p.
021007
.
33.
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
.
34.
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
.
35.
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
.
36.
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
.
37.
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
.
38.
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
.
39.
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
.
40.
Hooke
,
R.
, and
Jeeves
,
T. A.
,
1961
, “
Direct Search Solution of Numerical and Statistical Problems
,”
J. ACM
,
8
(
2
), pp.
212
229
.
41.
CWRU
,
2015
, “
Bearing Data Center
,”
Case Western University
, Cleveland, OH.
42.
NASAARC
,
2015
, “
IMS Bearings Data Set
,”
NASA Ames Research Center
, Moffett Field, CA.
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