Journal of Intelligent Manufacturing

, Volume 29, Issue 5, pp 1115–1131 | Cite as

Contribution of angular measurements to intelligent gear faults diagnosis

  • Semchedine Fedala
  • Didier Rémond
  • Rabah Zegadi
  • Ahmed Felkaoui


Currently, work on the automation of vibration diagnosis is mainly based on indicators extracted from Time sampled Acceleration signals. There are other attractive alternatives such as those based on Angle synchronized measurements, which can provide a considerable number of more relevant and diverse indicators and, thus, lead to better performance in gear fault classification. The diversity of angular measurements (Instantaneous Angular Speed, Transmission Error and Angular sampled Acceleration) represents potential sources of relevant information in fault detection and diagnosis systems. These complementary measurements of existing signals or new relevant signals allow the construction of Feature Vector (FV) offering robust and effective classification methods even for different or non-stationary running speed conditions. In this paper, we propose to build several FVs based on indicators derived from the angular techniques to compare them to the ones calculated from the time signals, proving their superior performance in detection and identification of gear faults. It will be a question to demonstrate the effectiveness of angular indicators in increasing classification performances, using a supervised classifier based on Artificial Neural Networks and thus determining the most suitable signals.


Fault diagnosis Gears Angular resampling Transmission Error Instantaneous Angular Speed Order spectra Artificial Neural Networks 



This work was achieved at the laboratories LaMCoS (INSA-Lyon, France) and LMPA (IOMP, Sétif -1- University, Algeria). The authors would like to thank the Algerian and French Ministries of Higher Education and Scientific Research for their financial and technical support in the framework of program PROFAS 2011-2012.


  1. André, H., Antoni, J., Daher, Z., & Rémond, D. (2010). Comparison between angular sampling and angular resampling methods applied on the vibration monitoring of a gear meshing in non stationary conditions. In Proceedings of the international conference on noise and vibration engineering, Belgium.Google Scholar
  2. Bendat, J. S., & Piersol, A. G. (1986). Random data: Analysis and measurement procedures (2nd ed.). New York: Wiley.Google Scholar
  3. Bonnardot, F., El Badaoui, M., Randall, B., Danière, J., & Guillet, F. (2005). Use of the acceleration of a gearbox in order to perform angular resampling with limited speed fluctuation. Mechanical Systems and Signal Processing, 19, 766–785.CrossRefGoogle Scholar
  4. Boulenger, A., & Pachaud, C. (2007). Analyse vibratoire en maintenance: Surveillance et diagnostic vibratoire. Paris: Edition Dunod, L’Usine Nouvelle.Google Scholar
  5. Drefus, G., Martinez, J. M., Samuelides, M., Gordon, M. B., Badran, F., Thiria, S., et al. (2002). Réseaux de neurones: Méthodologie et applications. Paris: Editions Eyrolles.Google Scholar
  6. Dubuisson, B. (1990). Diagnostic et reconnaissance des formes. Traité des nouvelles technologies. Série: Diagnostic et Maintenance. Paris: Editions Hermès.Google Scholar
  7. Fedala, S., Felkaoui, A., & Zegadi, R. (2009). Optimisation des paramètres du vecteur forme: Application au diagnostic vibratoire automatisé des défauts d’une boite de vitesse d’un hélicoptère. Journal Matériaux & Techniques, 97(2), 149–155.CrossRefGoogle Scholar
  8. Hajnayeb, A., Ghasemloonia, A., Khadem, S. E., & Moradi, M. H. (2011). Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Systems with Applications, 38(8), 10205–10209.CrossRefGoogle Scholar
  9. Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete fourier transforms. Proceedings of the IEEE, 66, 55–83.CrossRefGoogle Scholar
  10. Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River: Prentice Hall.Google Scholar
  11. Hoffman, A. J., & Van der Merwe, N. T. (2002). The application of neural networks to vibrational diagnostics for multiple fault conditions. Computer Standards & Interfaces, 24, 139–149.CrossRefGoogle Scholar
  12. Hunter, D., Yu, H., Pukish, M. S., Kolbusz, J., & Wilamowski, B. M. (2012). Selection of proper neural network sizes and architectures: A comparative study. IEEE Transactions on Industrial Informatics, 8(2), 228–240.CrossRefGoogle Scholar
  13. Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. (2011). Feature subset selection using differential evolution and a statistical repair mechanism. Expert Systems with Applications, 38(9), 11515–11526.Google Scholar
  14. Kudo, M., & Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1), 25–41.CrossRefGoogle Scholar
  15. Li, Y., Gu, F., Harris, G., Ball, A., Bennett, N., & Travis, K. (2005). The measurement of instantaneous angular speed. Mechanical Systems and Signal Processing, 19, 786–805.CrossRefGoogle Scholar
  16. Paya, B. A., Esat, I. I., & Badi, M. N. M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical Systems and Signal Processing, 11, 751–765.CrossRefGoogle Scholar
  17. Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. H. (2007). Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 21, 1746–1754.CrossRefGoogle Scholar
  18. Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Artificial neural network approach for fault detection in rotary system. Applied Soft Computing, 8(1), 740–748.CrossRefGoogle Scholar
  19. Randall, B. (2011). Vibration-based condition monitoring: industrial, aerospace and automotive applications. New York: Wiley.CrossRefGoogle Scholar
  20. Rémond, D. (1998). Practical performances of high-speed measurement of gear transmission error or torsional vibrations with optical encoders. Measurement Science & Technology, 9(3), 347–353.CrossRefGoogle Scholar
  21. Renaudin, L., Bonnardot, F., Musy, O., Doray, J. B., & Rémond, D. (2010). Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing, 24, 998–2011.CrossRefGoogle Scholar
  22. Rzeszucinski, P. J., Sinha, J. K., Edwards, R., Starr, A., & Allen, B. (2012). Normalised root mean square and amplitude of sidebands of vibration response as tools for gearbox diagnosis. Strain, 48, 445–452.CrossRefGoogle Scholar
  23. Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2004). Bearing fault detection using artificial neuralnetworks and genetic algorithm. Journal on Applied Signal Processing, 3, 366–377.Google Scholar
  24. Trigeassou, J. C. (2011). Diagnostic des machines électriques. Paris: Edition Lavoisier.CrossRefGoogle Scholar
  25. Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. New Jersey: Wiley.CrossRefGoogle Scholar
  26. Vecer, P., Kreidl, M., & Smid, R. (2005). Condition indicators for gearbox condition monitoring systems. ACTA Polytechnica, 45, 35–43.Google Scholar
  27. Zappalá, D., Tavner, P. J., Crabtree, C. J., & Sheng, S. (2014). Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis. IET Renewable Power Generation, 8(4), 380–389.CrossRefGoogle Scholar
  28. Zarei, J. (2012). Induction motors bearing fault detection using pattern recognition techniques. Expert Systems with Applications, 39, 68–73.Google Scholar
  29. Zwingelstein, G. (1995). Diagnostic des défaillances. Théorie et pratique pour les systèmes industriels, Série Diagnostic et Maintenance. Paris: Editions Hermès.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Semchedine Fedala
    • 1
  • Didier Rémond
    • 2
  • Rabah Zegadi
    • 1
  • Ahmed Felkaoui
    • 1
  1. 1.Applied Precision Mechanics Laboratory, Institute of Optics and Precision MechanicsSetif -1- UniversitySetifAlgeria
  2. 2.LaMCoS, INSA-LyonVilleurbanne CedexFrance

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