Journal of Failure Analysis and Prevention

, Volume 19, Issue 1, pp 98–105 | Cite as

An Automated Feature Extraction Algorithm for Diagnosis of Gear Faults

  • Muhammad IrfanEmail author
  • Nordin Saad
  • A. Alwadie
  • M. Awais
  • M. Aman Sheikh
  • A. Glowacz
  • V. Kumar
Technical Article---Peer-Reviewed


Gears are used for the transfer of mechanical power and are an important part of the electromechanical transmission system. Unexpected failure of gear could cause shutdown of the machines and proves to be expensive in terms of production loss and maintenance. Therefore, reliable condition monitoring is required to protect unexpected gear failures. It has been highlighted in the recently published literature that the gear faults appear at the specific gear frequencies in the instantaneous power spectrum of the motor. However, the amplitudes of these gear frequencies are very small and are shadowed by the environment noise. Thus, reliable diagnosis of gear faults is a challenge in real-time fault diagnosis systems. This issue has been addressed in this paper through the development of the automated spectral extraction algorithm. The theoretical investigation has been verified through the custom-designed experimental test rig.


Condition monitoring Preventive maintenance Gear fault classification 



The authors acknowledge the Najran University, Saudi Arabia, for providing funding and research facility. The authors also acknowledge the funding support by Universiti Teknologi PETRONAS, Malaysia, and Ministry of Higher Education, Malaysia, for the award of PRGS fund.


  1. 1.
    N.-T. Nguyen, H.-H. Lee, J.-M. Kwon, Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor. J. Mech. Sci. Technol. 22(3), 490–496 (2008)CrossRefGoogle Scholar
  2. 2.
    G.-M. Lim, D.-M. Bae, J.-H. Kim, Fault diagnosis of rotating machine by thermography method on support vector machine. J. Mech. Sci. Technol. 28(8), 2947–2952 (2014)CrossRefGoogle Scholar
  3. 3.
    D. Kateris, D. Moshou, X.-E. Pantazi, I. Gravalos, N. Sawalhi, S. Loutridis, A machine learning approach for the condition monitoring of rotating machinery. J. Mech. Sci. Technol. 28(1), 61–71 (2014)CrossRefGoogle Scholar
  4. 4.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, M. Magzoub, An intelligent fault diagnosis of induction motors in an arbitrary noisy environment. J. Nondestr. Test. Eval. 15(5), 730–736 (2015)Google Scholar
  5. 5.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.T. Hung, M.A. Magzoub, Analysis of bearing surface roughness defects in induction motors. J. Fail. Anal. Prev. 15(5), 730–736 (2015)CrossRefGoogle Scholar
  6. 6.
    A.M.G. Júnior, V.R. Silva, L.M.R. Baccarini, M.L.F. Reis, Three-phase induction motors faults recognition and classification using neural networks and response surface models. J. Control Autom. Electr. Syst. 25(3), 330–338 (2014)CrossRefGoogle Scholar
  7. 7.
    S.H. Kia, H. Henao, G.-A. Capolino, Development of a Test Bench Dedicated to Condition Monitoring of Wind Turbines (IEEE-IECON, Dallas, 2014)CrossRefGoogle Scholar
  8. 8.
    A.R. Mohanty, C. Kar, Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mech. Syst. Signal Process. 20(1), 158–187 (2006)CrossRefGoogle Scholar
  9. 9.
    N. Feki, G. Clerc, P. Velex, An integrated electro-mechanical model of motor-gear units: applications to tooth fault detection by electric measurements. Mech. Syst. Signal Process. 29, 377–390 (2012)CrossRefGoogle Scholar
  10. 10.
    E.G. Strangas, Response of electrical drives to gear and bearing faults-diagnosis under transient and steady state conditions, in Proceedings of Workshop on Electrical Machines Design Control and Diagnosis (WEMDCD), Invited paper, Paris, pp. 289–297 (2013)Google Scholar
  11. 11.
    H. Henao, S.H. Kia, G.-A. Capolino, Torsional vibration assessment and gear fault diagnosis in railway traction system. IEEE Trans. Ind. Electron. 58(5), 1707–1717 (2011)CrossRefGoogle Scholar
  12. 12.
    I. Bogiatzidis, A. Safacas, E. Mitronikas, Detection of backlash phenomena appearing in a single cement kiln drive using the current and the electromagnetic torque signature. IEEE Trans. Ind. Electron. 60(8), 3441–3453 (2013)CrossRefGoogle Scholar
  13. 13.
    Z. Wang, H. Jiang, Robust incipient fault identification of aircraft engine rotor based on wavelet and fraction. Aerosp. Sci. Technol. 14, 221–224 (2010)CrossRefGoogle Scholar
  14. 14.
    B. Akin, U. Orguner, H.A. Toliyat, M. Rayner, Phase-sensitive detection of motor fault signatures in the presence of noise. IEEE Trans. Ind. Electron. 55(6), 2539–2550 (2008)CrossRefGoogle Scholar
  15. 15.
    B. Akin, S.D. Choi, U. Orguner, H.A. Toliyat, A simple real-time fault signature monitoring tool for motor drive imbedded fault diagnosis systems. IEEE Trans. Ind. Electron. 58(5), 1990–2001 (2011)CrossRefGoogle Scholar
  16. 16.
    H.A. Toliyat, S. Nandi, S. Choi, H.S. Kelk, Electric machines, modelling, condition monitoring and fault diagnosis (CRC Press, Boca Raton, 2012)CrossRefGoogle Scholar
  17. 17.
    S. Rajagopalan, T.G. Habetler, R.G. Harley, J.A. Restrepo, J.M. Alle, Non-stationary motor fault detection using recent quadratic time-frequency representations. Int. Conf. Rec. IEEE IAS Ann. Meet. 5, 2333–2339 (2006)Google Scholar
  18. 18.
    C.J.C. Burges, A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)CrossRefGoogle Scholar
  19. 19.
    L.B. Jack, A.K. Nandi, Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mech. Syst. Signal Process. 16(2–3), 373–390 (2002)CrossRefGoogle Scholar
  20. 20.
    S. Abbasion, A. Rafsanjani, A. Farshidianfar, N. Irani, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Syst. Signal Process. 21(7), 2933–2945 (2007)CrossRefGoogle Scholar
  21. 21.
    V. Sugumaran, V. Muralidharan, K.I. Ramachandran, Feature selection using decision tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process. 21(2), 930–942 (2007)CrossRefGoogle Scholar
  22. 22.
    V. Sugumaran, G.R. Sabareesh, K.I. Ramachandran, Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Syst. Appl. 34(4), 3090–3098 (2008)CrossRefGoogle Scholar
  23. 23.
    DejieYu. YuYang, J. Cheng, A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40(9–10), 943–950 (2007)CrossRefGoogle Scholar
  24. 24.
    B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi, Artificial neural networks and genetic algorithm for bearing fault detection. Soft. Comput. 10(3), 264–271 (2006)CrossRefGoogle Scholar
  25. 25.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, M. Magzoub, An online fault diagnosis system for induction motors via instantaneous power analysis. Tribol. Trans. 60(4), 592–604 (2017)CrossRefGoogle Scholar
  26. 26.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.M. Nor, A. Alwadie, A hardware and software integration approach for development of a non-invasive condition monitoring systems for motor-coupled gears faults diagnosis. Commun. Comput. Inf. Sci. 751, 642–655 (2017)Google Scholar
  27. 27.
    A. Widodo, B.-S. Yang, Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21(6), 2560–2574 (2007)CrossRefGoogle Scholar
  28. 28.
    M. Awais, L. Chiari, E.A.F. Ihlen, J. Helbostad, L. Palmerini, Physical activity classification for elderly people in free living conditions. IEEE J. Biomed. Health Inform. (2018). Google Scholar
  29. 29.
    M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar
  30. 30.
    N. Saad, M. Irfan, R. Ibrahim, Condition Monitoring and Faults Diagnosis of Induction Motors: Electrical Signature Analysis (CRC Press, Routledge - Taylor & Francis Group, 2018)Google Scholar
  31. 31.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, M. Magzoub, A non-invasive method for condition monitoring of induction motors operating under arbitrary loading conditions. Arab. J. Sci. Eng. 41, 3463–3471 (2016)CrossRefGoogle Scholar
  32. 32.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, A. Alwadie, M. Aman, An assessment on the non-invasive methods for condition monitoring of induction motors, in Fault Diagnosis and Detection (InTech Publishing, 2017)Google Scholar
  33. 33.
    M. Aman Sheikh, N. Nor, T. Ibrahim, S. Tahir Bakhsh, M. Irfan, H. Binti Daud, Non-invasive methods for condition monitoring and electrical fault diagnosis of induction motors, in Fault Diagnosis and Detection (InTech Publishing, 2017)Google Scholar

Copyright information

© ASM International 2018

Authors and Affiliations

  • Muhammad Irfan
    • 1
    Email author
  • Nordin Saad
    • 2
  • A. Alwadie
    • 1
  • M. Awais
    • 3
  • M. Aman Sheikh
    • 2
  • A. Glowacz
    • 4
  • V. Kumar
    • 5
  1. 1.Electrical Engineering DepartmentNajran UniversityNajranKingdom of Saudi Arabia
  2. 2.Department of Electrical and Electronic EngineeringUniversiti Teknologi PETRONASSeri IskanderMalaysia
  3. 3.Department of Electrical, Electronic, and Information Engineering, Guglielmo MarconiUniversity of BolognaBolognaItaly
  4. 4.AGH University of Science and TechnologyKrakówPoland
  5. 5.Department of Electrical and Electronics EngineeringKarunya UniversityCoimbatoreIndia

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