Abstract
In the naval and offshore industry, many rotating equipment use rolling bearings and any fault can cause a high impact on the costs related to a possible equipment failure, which are far superior to the cost of replacing the bearing itself. Several reasons can shorten the life of the bearings and lead them to sudden breaks. In this context, the use of fault detection techniques in rolling bearings is extremely useful. The objective of this work is to apply the spectral kurtosis technique to select the frequency bandwidth, which contains the fault signature, for fault detection in three (3) rolling bearings. Bearing # 1 has fault in the cage, bearing # 2 has a fault in the outer race and bearing # 3 has two faults, one in the rolling element (ball) and another in the cage. Based on the difference found between the theoretical fault frequencies and the fault frequencies obtained experimentally, all faults were correctly identified. This difference does not exceed 1.83% in any case.
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Abbreviations
- α :
-
Contact angle (radial)
- \(\mu \left(n\right)\) :
-
Error signal for the autoregressive (AR) model
- \({v}_{0}(n)\) :
-
Noise signal
- \(a\left(k\right)\) :
-
Prediction coefficients
- d(n):
-
Random vibration signal
- \({D}_{\text{p}}\) :
-
Rolling elements diameter
- \({D}_{\text{b}}\) :
-
Primitive rolling bearing diameter
- \(e\left(n\right)\) :
-
Error (residual) signal
- \(E\left(f\right)\) :
-
Fourier transform of the error (residual) signal
- \({f}_{\text{BPFI}}\) :
-
Characteristic defect frequency for the inner ring
- \({f}_{\text{BPFO}}\) :
-
Characteristic defect frequency for the outer ring
- \({f}_{\text{FTF}}\) :
-
Characteristic defect frequency for the cage
- \({f}_{\text{BSF}}\) :
-
Characteristic defect frequency for the balls
- \(g(t)\) :
-
Impulse responses
- H (t, f):
-
Envelope of the Short-Time Fourier Transform
- \(K\left(f\right)\) :
-
Spectral kurtosis.
- \(\mathrm{\rm M}\left(f\right)\) :
-
Fourier transform of \(\mu \left(n\right)\)
- \({n}_{\text{b}}\) :
-
Number of rolling elements
- \(p(k)\) :
-
Cross-correlation
- \(s\left(n\right)\) :
-
Vibration signal without noise
- \({\mathrm{v}}_{1}(n)\) :
-
Reference signal
- \({\widehat{w}}_{k}(n)\) :
-
Adaptive filter coefficients
- W(n):
-
Time window
- \(X(t,f)\) :
-
Short-time Fourier transform of x(t)
- \(y\left(n\right)\) :
-
Output signal for the ANC filter
- \(z\left(n\right)\) :
-
Output signal for the autoregressive (AR) model
References
R. Dwyer, Detection of non-Gaussian signals by frequency domain Kurtosis estimation. ICASSP ’83. IEEE Int. Conf Acoust. Speech Signal Process. 8, 607–610 (1983)
R.F. Dwyer, Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals. IEEE J. Ocean. Eng. 9, 85–92 (1984)
R.F. Dwyer, Asymptotic detection performance of discrete power and higher-order spectra estimates. IEEE J. Ocean. Eng. 10, 303–315 (1985)
C. Ottonello, S. Pagnan, Modified frequency domain kurtosis for signal processing. Electron. Lett. 30, 1117–1118 (1994)
R.B. Randall, J. Antoni, Rolling element bearing diagnostics: a tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011)
J. Antoni, The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20, 282–307 (2006)
J. Antoni, R.B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20, 308–331 (2006)
P.C. Büchner, “Análise de Mancais de Rolamentos através dos Sinais de Vibrações, Usando Método da Curtose e do Envelope”. Dissertação de Mestrado, Pontifícia Universidade Católica do Paraná, Curitiba-PR (2001)
J. Antoni, R.B. Randall, Unsupervised noise cancellation for vibration signals: part II: a novel frequency-domain algorithm. Mech. Syst. Signal Process. 18, 103–117 (2004)
N. Sawalhi, Diagnostics, prognostics and fault simulation for rolling element bearings. Ph.D. Thesis, University of New South Wales, Australia (2007)
S.M. Kay, S.L. Marple, Spectrum analysis: a modern perspective. Proc. IEEE 69, 1380–1419 (1981)
P.D. McFadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing. J. Sound Vib. 96, 69–82 (1984)
R.B. Randall, Developments in digital analysis techniques for diagnostics of bearings and gears. In: Fifth International Congress on Sound and Vibration (International Institute of Acoustics and Vibration, 1997).
I. Howard, I.M. Howard, Defence Science and Technology Organisation (Australia) and Aeronautical and Maritime Research Laboratory (Australia). A review of rolling element bearing vibration: detection, diagnosis and prognosis/Ian Howard DSTO Aeronautical and Maritime Research Laboratory Melbourne (1994)
R.B. Randall, Frequency Analysis. (Bruel & Kjaer, 1987), Glostrup (DK), pp. 157–161.
Acknowledgements
This research was supported by the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Funding of Studies and Projects (FINEP) and the Ministry of Science, Technology and Innovation (MCTI) through the Program of Human Resources for the Oil and Gas Sector (PRH-ANP/MCTI).
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Osorio Santander, E.J., Silva Neto, S.F., Vaz, L.A. et al. Using spectral kurtosis for selection of the frequency bandwidth containing the fault signature in rolling bearings. Mar Syst Ocean Technol 15, 243–252 (2020). https://doi.org/10.1007/s40868-020-00084-2
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DOI: https://doi.org/10.1007/s40868-020-00084-2