Extraction of effective information from measured vibration signals is a fundamental task for the machinery condition monitoring and fault diagnosis. As a typical blind source separation (BSS) method, independent component analysis (ICA) is known to be able to effectively extract the latent information in complex signals even when the mixing mode and sources are unknown. In this paper, we propose a novel approach to overcome two major drawbacks of the traditional ICA algorithm: lack of robustness and source contribution evaluation. The enhanced ICA algorithm is established to escalate the separation performance and robustness of ICA algorithm. This algorithm repeatedly separates the mixed signals multiple times with different initial parameters and evaluates the optimal separated components by the clustering evaluation method. Furthermore, the source contributions to the mixed signals can also be evaluated. The effectiveness of the proposed method is validated through the numerical simulation and experiment studies.

References

References
1.
Wang
,
Y.
,
He
,
Z.
, and
Zi
,
Y.
, 2010, “
Enhancement of Signal Denoising and Multiple Fault Signatures Detecting in Rotating Machinery Using Dual-Tree Complex Wavelet Transform
,”
Mech. Syst. Signal Process.
,
24
(
1
), pp.
119
137
.
2.
Wang
,
Y.
,
He
,
Z.
, and
Zi
,
Y.
, 2009, “
A Demodulation Method Based on Improved Local Mean Decomposition and Its Application in Rub-Impact Fault Diagnosis
,”
Meas. Sci. Technol.
,
20
(
2
),
025704
.
3.
Wang
,
Y.
,
He
,
Z.
, and
Zi
,
Y.
, 2010, “
A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis
,”
ASME J. Vibr. Acoust.
,
132
(
2
), p.
021010
.
4.
Lei
,
Y.
,
He
,
Z.
, and
Zi
,
Y.
, 2009, “
Application of the EEMD Method to Rotor Fault Diagnosis of Rotating Machinery
,”
Mech. Syst. Signal Process.
,
23
(
4
), pp.
1327
1338
.
5.
Wang
,
X.
,
Zi
,
Y.
, and
He
,
Z.
, 2009, “
Multiwavelet Construction via an Adaptive Symmetric Lifting Scheme and Its Applications for Rotating Machinery Fault Diagnosis
,”
Meas. Sci. Technol.
,
20
(
4
), p.
045103
.
6.
Yuan
,
J.
,
He
,
Z.
,
Zi
,
Y.
,
Lei
,
Y.
, and
Li
,
Z.
, 2009, “
Adaptive Multiwavelets via Two-Scale Similarity Transforms for Rotating Machinery Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
23
(
5
), pp.
1490
1508
.
7.
Yuan
,
J.
,
He
,
Z.
, and
Zi
,
Y.
, 2010, “
Gear Fault Detection Using Customized Multiwavelet Lifting Schemes
,”
Mech. Syst. Signal Process.
,
24
(
5
), pp.
1509
1528
.
8.
Tan
,
J.
,
Chen
,
X.
,
Wang
,
J.
,
Chen
,
H.
,
Cao
,
H.
,
Zi
,
Y.
, and
He
,
Z.
, 2009, “
Study of Frequency-Shifted and Re-Scaling Stochastic Resonance and Its Application to Fault Diagnosis
,”
Mech. Syst. Signal Process.
,
23
(
3
), pp.
811
822
.
9.
Lee
,
S. K.
,
Mace
,
B. R.
, and
Brennan
,
M. J.
, 2007, “
Wave Propagation, Reflection and Transmission in Curved Beams
,”
J. Sound Vib.
,
306
(
3–5
), pp.
636
656
.
10.
Senjanovic
,
I.
,
Tomasevic
,
S.
, and
Vladimir
,
N.
, 2009, “
An Advanced Theory of Thin-Walled Girders With Application to Ship Vibrations
,”
Mar. Struct.
,
22
(
3
), pp.
387
437
.
11.
Niu
,
J.
,
Song
,
K.
, and
Lim
,
C. W.
, 2005, “
On Active Vibration Isolation of Floating Raft System
,”
J. Sound Vib.
,
285
(
1–2
), pp.
391
406
.
12.
Otrin
,
M.
, and
Boltezar
,
M.
, 2009, “
On the Modeling of Vibration Transmission Over a Spatially Curved Cable With Casing
,”
J. Sound Vib.
,
325
(
4–5
), pp.
798
815
.
13.
Lee
,
Y. Y.
,
Su
,
R. K. L.
,
Ng
,
C. F.
, and
Hui
,
C. K.
, 2009, “
The Effect of Modal Energy Transfer on the Sound Radiation and Vibration of a Curved Panel: Theory and Experiment
,”
J. Sound Vib.
,
324
(
3–5
), pp.
1003
1015
.
14.
Xie
,
S.
,
Or
,
S. W.
,
Lai Wa Chan
,
H.
, Kong
Choy
,
P.
, and
Chou Kee Liu
,
P.
, 2007, “
Analysis of Vibration Power Flow From a Vibrating Machinery to a Floating Elastic Panel
,”
Mech. Syst. Signal Process.
,
21
(
1
), pp.
389
404
.
15.
Bonfiglio
,
P.
,
Pompoli
,
F.
,
Peplow
,
A. T.
, and
Nilsson
,
A. C.
, 2007, “
Aspects of Computational Vibration Transmission for Sandwich Panels
,”
J. Sound Vib.
,
303
(
3–5
), pp.
780
797
.
16.
Efimtsov
,
B. M.
, and
Lazarev
,
L. A.
, 2009, “
Forced Vibrations of Plates and Cylindrical Shells With Regular Orthogonal System of Stiffeners
,”
J. Sound Vib.
,
327
(
1–2
), pp.
41
54
.
17.
Jutten
,
C.
, and
Herault
,
J.
, 1991, “
Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic Architecture
,”
Signal Process.
,
24
(
1
), pp.
1
10
.
18.
Comon
,
P.
, 1994, “
Independent Component Analysis, a New Concept?
,”
Signal Process.
,
36
(
3
), pp.
287
314
.
19.
Hyvärinen
,
A.
, and
Oja
,
E.
, 1997, “
A Fast Fixed-Point Algorithm for Independent Component Analysis
,”
Neural Comput.
,
9
(
7
), pp.
1483
1492
.
20.
Hyvärinen
,
A.
, 1999, “
Fast and Robust Fixed-Point Algorithms for Independent Component Analysis
,”
IEEE Trans. Neural Netw.
,
10
(
3
), pp.
626
634
.
21.
Hyvärinen
,
A.
,
Karhunen
,
J.
, and
Oja
,
E.
, 2001,
Independent Component Analysis
,
John Wiley and Sons
,
New York
.
22.
Hu
,
H.
, 2008, “
ICA-Based Neighborhood Preserving Analysis for Face Recognition
,”
Comput. Vis. Image Underst.
,
112
(
3
), pp.
286
295
.
23.
Correa
,
N.
,
Adali
,
T.
, and
Calhoun
,
V. D.
, 2007, “
Performance of Blind Source Separation Algorithms for fMRI Analysis Using a Group ICA Method
,”
Magn. Reson. Imaging
,
25
(
5
), pp.
684
694
.
24.
Ye
,
Y.
,
Zhang
,
Z.
-L.,
Zeng
,
J.
, and
Peng
,
L.
, 2008, “
A Fast and Adaptive ICA Algorithm With Its Application to Fetal Electrocardiogram Extraction
,”
Appl. Math. Comput.
,
205
(
2
), pp.
799
806
.
25.
Xie
,
L.
, and
Wu
,
J.
, 2006, “
Global Optimal ICA and Its Application in MEG Data Analysis
,”
Neurocomputing
,
69
(
16–18
), pp.
2438
2442
.
26.
Zuo
,
M. J.
,
Lin
,
J.
, and
Fan
,
X.
, 2005, “
Feature Separation Using ICA for a One-Dimensional Time Series and Its Application in Fault Detection
,”
J. Sound Vib.
,
287
(
3
), pp.
614
624
.
27.
Moussaoui
,
S.
,
Hauksdóttir
,
H.
,
Schmidt
,
F.
,
Jutten
,
C.
,
Chanussot
,
J.
,
Brie
,
D.
,
Douté
,
S.
, and
Benediktsson
,
J. A.
, 2008, “
On the Decomposition of Mars Hyperspectral Data by ICA and Bayesian Positive Source Separation
,”
Neurocomputing
,
71
(
10–12
), pp.
2194
2208
.
28.
Kwak
,
N.
,
Kim
,
C.
, and
Kim
,
H.
, 2008, “
Dimensionality Reduction Based on ICA for Regression Problems
,”
Neurocomputing
,
71
(
13–15
), pp.
2596
2603
.
29.
Himberg
,
J.
,
Hyvärinen
,
A.
, and
Esposito
,
F.
, 2004, “
Validating the Independent Components of Neuroimaging Time Series via Clustering and Visualization
,”
Neuroimage
,
22
(
3
), pp.
1214
1222
.
30.
Hyvärinen
,
A.
, and
Oja
,
E.
, 2000, “
Independent Component Analysis: Algorithms and Applications
,”
Neural Networks
,
13
(
4–5
), pp.
411
430
.
31.
Himberg
,
J.
, and
Hyvärinen
,
A.
, 2003, “
ICASSO: Software for Investigating the Reliability of ICA Estimates by Clustering and Visualization
,”
IEEE XIII Workshop on Neural Networks for Signal Processing—NNSP’03
, pp.
259
268
.
32.
Mead
,
D. J.
, and
Bardell
,
N. S.
, 1986, “
Free Vibration of a Thin Cylindrical Shell With Discrete Axial Stiffeners
,”
J. Sound Vib.
,
111
(
2
), pp.
229
250
.
You do not currently have access to this content.