Industrial Bearing Fault Detection Using Time-Frequency Analysis


  • Y. Bella Electrical Engineering Department, Ferhat Abbes Setif 1 University, Setif, Algeria
  • A. Oulmane Department of Mechanical Engineering, Polytechnic School of Montreal, Canada
  • M. Mostefai Electrical Engineering Department, Ferhat Abbes Setif 1 University, Setif, Algeria
Volume: 8 | Issue: 4 | Pages: 3294-3299 | August 2018 |


Time-frequency fault detection techniques were applied in this study, for monitoring real life industrial bearing. For this aim, an experimental test bench was developed to emulate the bearing rotating motion and to measure the induced vibration signals. Dedicated software was used to analyze the acquired measurements in the time-frequency domain using several distributions with varying resolution. Results showed that each fault type exhibits a specific behavior in the time-frequency domain, which is exploited in the localization of the faulty component.


time-frequency domain, industrial bearing, nondestructive test, vibration analysis


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E. L. Bonaldi, G. Lambert-Torresm, J. G. B. da Silva, L. E. de Lacerda de Oliveira, L. E. B. da Silva, “Predictive maintenance by electrical signature analysis to induction motors”, available at:, INTECH Open Access Publisher, 2012 DOI:

S. Patidar, P. Kumar Soni, “An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults”, International Journal of Engineering Trends and Technology, Vol. 4, No. 5, pp. 1803-1809, 2013

S. Nandi, H. A. Toliyat, X. Li, “Condition monitoring and fault diagnosis of electrical motors-a review”, IEEE Transactions on Energy Conversion, Vol. 20, No. 4, pp. 719-729, 2005 DOI:

S. Choi, B. Akin, M. M. Rahimian, H. A. Toliyat, “Implementation of a fault-diagnosis algorithm for induction machines based on advanced digital-signal-processing techniques”, IEEE Transactions on Industrial Electronics, Vol. 58, No. 3, pp. 937-948, 2011 DOI:

F. E. H. Montero, O. C. Medina, “The application of bispectrum on diagnosis of rolling element bearings: A theoretical approach”, Mechanical Systems and Signal Processing, Vol. 22, No. 3, pp. 588-596, 2008 DOI:

S. Guoji, S. McLaughlin, X. Yongcheng, P. White, “Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears”, Mechanical Systems and Signal Processing, Vol. 43, No. 1, pp. 76-89, 2014 DOI:

C. Li, V. Sanchez, G. Zurita, M. C. Lozada, D. Cabrera, “Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement”, ISA Transactions, Vol. 60, pp. 274-184, 2015 DOI:

L. Cohen, Time-Frequency Analysis, Prentice Hall, 1995

S. Qian, D. Chen, Joint Time-Frequency Analysis, Prentice Hall, 1996

P. Flandrin, Time-Frequency/Time-Scale Analysis, Academic press, 1998

A. Prudhom, J. Antonino-Daviu, H. Razik, V. Climente-Alarcon, “Time- frequency vibration analysis for the detection of motor damages caused by bearing currents”, Mechanical Systems and Signal Processing, Vol. 84, No. A, pp. 747-762, 2015 DOI:

J. Chen, J. Pan, Z. Li, Y. Zi, X. Chen, “Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals”, Renewable Energy, Vol. 89, pp. 80-92, 2016 DOI:

N. Hess-Nielsen, M. V. Wickerhauser, “Wavelets and time-frequency analysis”, Proceedings of the IEEE, Vol. 84, No. 4, pp.523-540, 1996 DOI:

L. Cohen, “Time-frequency distributions-a review”, Proceedings of the IEEE, Vol. 77, No. 7, pp. 941-981, 1989 DOI:

O. Rioul, P. Flandrin, “Time-scale energy distributions: A general class extending wavelet transforms”, IEEE Transactions on Signal Processing, Vol. 40, No. 7, pp. 1746-1757, 1992 DOI:

P. Flandrin, P. Goncalves, “Geometry of affine time–frequency distributions”, Applied and Computational Harmonic Analysis, Vol. 3, No. 1, pp. 10-39, 1996 DOI:

A. Bermanis, G. Wolf, A. Averbuch, “Diffusion-based kernel methods on euclidean metric measure spaces”, Applied and Computational Harmonic Analysis, Vol. 41, No. 1, pp. 190-213, 2016 DOI:

L. Gelman, I. Petrunin, J. Komoda, “The new chirp-wigner higher order spectra for tran- sient signals with any known nonlinear frequency variation”, Mechanical Systems and Signal Processing, Vol. 24, No. 2, pp. 567–571, 2010 DOI:

L. Saidi, F. Fnaiech, H. Henao, G. A. Capolino, G. Cirrincione, “Diagnosis of broken-bars fault in induction machines using higher order spectral analysis”, ISA Transactions, Vol. 52, No. 1, pp. 140-148, 2013 DOI:

Z. Feng, M. Liang, F. Chu, “Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples”, Mechanical Systems and Signal Processing, Vol. 38, No. 1, pp. 165-205, 2013 DOI:

A. Rai, S. H. Upadhyay, “A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings”, Tribology International, Vol. 96, pp. 289-306, 2016 DOI:

C. Yang, T. J. Kang, D. Hyun, S. B. Lee, J. A. Antonino-Daviu, J. Pons-Llinares, “Reliable detection of induction motor rotor faults under the rotor axial air duct influence”, , IEEE Transactions on Industry Applications, Vol. 50, No. 4, pp. 2493-2502, 2014 DOI:

J. A. Antonino-Daviu, V. Climente-Alarcon, J. Pons-Llinares, E. J. Wiedenbrug, “Advanced rotor assessment of motors operating under variable load conditions in mining facilities”, 2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, USA, pp. 617-621, November 13, 2014 DOI:

M. Thomas, Fiabilite, Maintenance Predictive et Vibration des Machines, PUQ, 2012 (in French) DOI:

B. Boashash, Time-Frequency Signal Analysis and Processing: A Comprehensive Reference,Academic Press, 2015

C. Migeon, Emission Acoustique et Analyse Vibratoire Pour l’ Etude des Defauts de Roulements pour Differents Regimes Moteurs, MSc Thesis, Universite de Reims Champagne-Ardenne, 2011 (in French)


How to Cite

Y. Bella, A. Oulmane, and M. Mostefai, “Industrial Bearing Fault Detection Using Time-Frequency Analysis”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 4, pp. 3294–3299, Aug. 2018.


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