Due to importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method have good computational efficiency, and have good resolution in both, time and frequency domain. The point of interest in this investigation is the present of an effective method for multi fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The outcome that we have achieved from wavelet analysis and SVM are fully in agreement with empirical result.
1.
V. Purushotham, S. Narayanan, Suryanarayana A.N. Prasad. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT&E International (2005) 1–11
2.
D. BENTLY, Bently Nevada Co., Predictive maintenance through the monitoring and diagnostics of rolling element bearings, Applications Note ANO44 (1989) pp. 2–8.
3.
Wang
W. J.
McFadden
P. D.
Application of wavelets to gearbox vibration signals for fault detection
, Journal of Sound and Vibration
192
(1996
) 927
–939
.4.
Shiroishi
J.
Li
Y.
Liang
S.
Kurfess
T.
Danyluk
S.
Bearing condition diagnostics via vibration and acoustic emission measurements
, Mechanical Systems and Signal Processing
11
(1997
) 693
–705
5.
Scholkopf
B.
SVMs_apractical consequence of learning theory
, IEEE Intelligent systems
13
(1998
) 18
–19
6.
Dellomo
M. R.
Helicopter gearbox fault detection: a neural network-based approach. Transaction of the ASME
, Journal of Vibration and Acoustic
121
(1999
) 265
–272
7.
Li
B.
Chow
M. Y.
Tipsuwan
Y.
Hung
J. C.
Neural-Network-Based Motor Rolling Bearing Fault Diagnosis
, IEEE Transactions on industrial electronics.
Vol. 47
. No. 5
. October (2000
) 1060
–10
8.
L.B. Jack, A.K. Nandi, Support vector machines for detection and characterization of rolling element bearing faults, Proceedings of Institution of Mechanical Engineers, Part C; Journal of Mechanical Engineering Science 215 (2001) 1065–1074
9.
Nikolaou
N. G.
Antoniadis
I. A.
Rolling element bearing fault diagnosis using wavelet packets
, NDT&E International
35
(2002
) 197
–205
10.
Samanta
B.
Al-Balushi
K. R.
Al-Araimi
S. A.
Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection
, Engineering Application of Artificial Intelligent
16
(2003
) 657
–665
11.
A.M. Al-Ghamd, D. Mba, A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size, Mechanical Systems and Signal Processing
12.
A.K. Nandi, 2000a, Advanced digital vibration signal processing for condition monitoring, Proceedings of COMADEM 2000, Houston, TX, USA (2000) 129–143
13.
L.B. Jack, A.K. Nandi, Comparison of neural networks and support vector machines in condition monitoring applications, Proceeding of COMADEM 2000, Houston, TX, USA (2000) 721–730
14.
Jack
L. B.
Nandi
A. K.
2000
b, Genetic algorithm for feature extraction in machine condition monitoring with vibration signals
, IEE Proceeding of vision & Image Signal Processing
147
(2000) 205
–212
15.
Jack
L. B.
Nandi
A. K.
Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms
, Journal of Mechanical Systems and Signal Processing
16
(2002
) 373
–390
16.
A. Rojas, A.K. Nandi, Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines, Mechanical Systems and Signal Processing (2005)
17.
K. Ragulskis and A. Yurkauskas, Vibration of Bearings. Bristol, PA:Hemisphere, 1989.
18.
G. Lipovszky, K. Solyomvari, and G. Varga, Vibration Testing of Machines and Their Maintenance. Amsterdam, The Netherlands: Elsevier, 1990.
19.
J. I. Taylor, The Vibration Analysis Handbook. Tampa, FL: Vibration Consultants, 1994.
20.
T. A. Harris, Rolling Bearing Analysis. New York: Wiley, 1991.
21.
S. Lu, “A transfer matrix method for nonlinear vibration analysis of rotor-bearing systems,” presented at the 1991 ASME Design Tech. Conf.—13th Biennial Conf. Mechanical Vibration and Noise, Miami, FL, Sept. 22–25, 1991, pp. 75–83.
22.
N. F. Rieger and J. F. Crofoot, Vibrations of Rotating Machinery. Rochester, NY: Rochester Inst. of Technol., 1977.
23.
Strader
J.
A vibration mathematics primer
,” Sound Vib.
, vol. 29
, no. 6
, pp. 18
–23
, June 1995
.24.
M. Misti, Y. Misti, G. Oppnheim and J. Poggi, Wavelet Toolbox User’s Guide, The Mathworks, Inc. (2002)
25.
Donoho, D. L., 1995, “De-noising by Soft-Thresholding,” IEEE Trans. Inf. Theory, 41~3!, pp. 613–627
26.
Coifman
R. R
Wickerhauser
M. V.
Entropy-based algorithms for best basis selection
, IEEE Trans. on Inf. Theory
, vol. 38
, 2
(1992
) 713
–718
27.
V. Vapnik, The nature of statistical Learning Theory, Springer Verlag (1995) 128–136
28.
V. Vapnik, Statistical Learning Theory, Wiley, (1998)
29.
Huang
H. P.
Liu
Y. H.
Fuzzy Support Vector Machine for Pattern Recognition and Data Mining
, International Journal of Fuzzy Systems.
Vol 4
, No. 3
, (2002
) 826
–835
This content is only available via PDF.
Copyright © 2006
by ASME
You do not currently have access to this content.
