A systematic experimental study is presented in this paper on evaluating the effectiveness of a unified, multidomain algorithm for defect feature extraction in bearing condition monitoring and health diagnosis. The algorithm decomposes vibration signals measured on bearings by discrete wavelet transform and subsequently performs the Fourier transform on the wavelet coefficients. The effectiveness of such a unified technique is demonstrated through experimental case studies, which confirmed its advantage over the wavelet or Fourier transform techniques employed alone. Also, the unified technique has shown to be computationally more efficient than the enveloping technique based on continuous wavelet transform, thus providing a good signal processing tool for bearing defect diagnosis.
1.
Orhan
, S.
, Aktürk
, N.
, and Çelik
, V.
, 2006, “Vibration Monitoring for Defect Diagnosis of Rolling Element Bearings as a Predictive Maintenance Tool: Comprehensive Case Studies
,” NDT & E Int.
, 39
, pp. 293
–298
. 0963-86952.
Sun
, Q.
, Chen
, P.
, Zhang
, D.
, and Xi
, F.
, 2004, “Pattern Recognition for Automatic Machinery Fault Diagnosis
,” ASME J. Vibr. Acoust.
0739-3717, 126
(2
), pp. 307
–316
.3.
Dron
, J. P.
, Bolaers
, F.
, and Rasolofondraibe
, I.
, 2004, “Improvement of the Sensitivity of the Scalar Indicators (Crest Factor, Kurtosis) Using a Denoising Method by Spectral Subtraction: Application to the Detection of Defects in Ball Bearings
,” J. Sound Vib.
0022-460X, 270
(1–2
), pp. 61
–73
.4.
Cavacece
, M.
, and Introini
, A.
, 2002, “Analysis of Damage of Ball Bearings of Aeronautical Transmissions by Auto-Power Spectrum and Cross-Power Spectrum
,” ASME J. Vibr. Acoust.
0739-3717, 124
(2
), pp. 180
–185
.5.
Tse
, P. W.
, Peng
, Y. H.
, and Yam
, R.
, 2001, “Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities
,” ASME J. Vibr. Acoust.
0739-3717, 123
(3
), pp. 303
–310
.6.
Boltežar
, M.
, and Slavič
, J.
, 2006, “Fault Detection of DC Electric Motors Using the Bispectral Analysis
,” Meccanica
0025-6455, 41
(3
), pp. 283
–297
.7.
Mol
, H. A.
, and Van Nijen
, G. C.
, 1997, “Method for Analyzing Regularly Recurring Mechanical Vibrations
,” U.S. Patent No. 5,698,788.8.
Mori
, K.
, Kasashima
, N.
, Yoshioka
, T.
, and Ueno
, Y.
, 1996, “Prediction of Spalling on a Ball Bearing by Applying the Discrete Wavelet Transform to Vibration Signals
,” Wear
0043-1648, 195
, pp. 162
–168
.9.
Li
, C. J.
, and Ma
, J.
, 1997, “Wavelet Decomposition of Vibrations for Detection of Bearing-Localized Defects
,” NDT & E Int.
0963-8695, 30
(3
), pp. 143
–149
.10.
Prabhakar
, S.
, Mohanty
, A. R.
, and Sekhar
, A. S.
, 2002, “Application of Discrete Wavelet Transform for Detection of Ball Bearing Race Faults
,” Tribol. Int.
0301-679X, 35
, pp. 793
–800
.11.
Lin
, J.
, Zuo
, M. J.
, and Fyfe
, K. R.
, 2004, “Mechanical Fault Detection Based on the Wavelet De-Noising Technique
,” ASME J. Vibr. Acoust.
0739-3717, 126
(1
), pp. 9
–16
.12.
Shi
, D. F.
, Wang
, W. J.
, and Qu
, L. S.
, 2004, “Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform
,” ASME J. Vibr. Acoust.
0739-3717, 126
(4
), pp. 567
–573
.13.
Nikolaou
, N. G.
, and Antoniadis
, I. A.
, 2002, “Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets
,” Mech. Syst. Signal Process.
0888-3270, 16
(4
), pp. 677
–694
.14.
Chiementin
, X.
, Bolaers
, F.
, and Dron
, J.
, 2007, “Early Detection of Fatigue Damage on Rolling Element Bearings Using Adapted Wavelet
,” ASME J. Vibr. Acoust.
0739-3717, 129
(4
), pp. 495
–506
.15.
Wang
, C.
, Gao
, R. X.
, and Yan
, R.
, 2009, “Unified Time–Scale–Frequency Analysis for Machine Defect Signature Extraction: Theoretical Framework
,” Mech. Syst. Signal Process.
0888-3270, 23
(1
), pp. 226
–235
.16.
SKF Company
, 1996, SKF Bearing Maintenance Handbook, Denmark.17.
Yan
, R.
, and Gao
, R.
, 2003, “A Hybrid Signal Processing Approach to Sensor Data Analysis
,” Proceeding of the ASME International Mechanical Engineering Congress and Exposition (IMECE), Symposium on Intelligent Sensors and Sensor Networks
, Washington, DC, pp. 1159
–1166
.18.
Holm-Hansen
, B. T.
, Gao
, R. X.
, and Zhang
, L.
, 2004, “Customized Wavelet for Bearing Defect Detection
,” ASME J. Dyn. Syst., Meas., Control
0022-0434, 126
(4
), pp. 740
–745
.19.
Fu
, S.
, Muralikrishna
, B.
, and Raja
, J.
, 2003, “Engineering Surface Analysis With Different Wavelet Bases
,” ASME J. Manuf. Sci. Eng.
1087-1357, 125
(4
), pp. 844
–852
.20.
Zanardelli
, W. G.
, Strangas
, E. G.
, Khalil
, H. K.
, and Miller
, J. M.
, 2005, “Wavelet-Based Methods for the Prognosis of Mechanical and Electrical Failures in Electric Motors
,” Mech. Syst. Signal Process.
0888-3270, 19
, pp. 411
–426
.21.
Harris
, T.
, 1991, Rolling Bearing Analysis
, Wiley
, New York
.Copyright © 2009
by American Society of Mechanical Engineers
You do not currently have access to this content.
