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Frontiers of Mechanical Engineering

, Volume 10, Issue 3, pp 277–286 | Cite as

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

  • Diego CabreraEmail author
  • Fernando Sancho
  • René-Vinicio Sánchez
  • Grover Zurita
  • Mariela Cerrada
  • Chuan Li
  • Rafael E. Vásquez
Research Article

Abstract

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters’ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

Keywords

fault diagnosis spur gearbox wavelet packet decomposition random forest 

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References

  1. 1.
    Walha L, Fakhfakh T, Haddar M. Backlash effect on dynamic analysis of a two-stage spur gear system. Journal of Failure Analysis and Prevention, 2006, 6(3): 60–68CrossRefGoogle Scholar
  2. 2.
    Abbes M S, Fakhfakh T, Haddar M, et al. Effect of transmission error on the dynamic behaviour of gearbox housing. International Journal of Advanced Manufacturing Technology, 2007, 34(3–4): 211–218CrossRefGoogle Scholar
  3. 3.
    Tian Z, Zuo M, Wu S. Crack propagation assessment for spur gears using model-based analysis and simulation. Journal of Intelligent Manufacturing, 2012, 23(2): 239–253CrossRefGoogle Scholar
  4. 4.
    Ebersbach S, Peng Z. Fault diagnosis of gearbox based on monitoring of lubricants, wear debris, and vibration. In: Wang Q, Chung Y W, eds. Encyclopedia of Tribology. New York: Springer, 2013, 1059–1064CrossRefGoogle Scholar
  5. 5.
    Rgeai M, Gu F, Ball A, et al. Gearbox fault detection using spectrum analysis of the drive motor current signal. In: Kiritsis D, Emmanouilidis C, Koronios A, et al., eds. Engineering Asset Lifecycle Management. London: Springer, 2010, 758–769CrossRefGoogle Scholar
  6. 6.
    Hong L, Dhupia J S. A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, 2014, 333(7): 2164–2180CrossRefGoogle Scholar
  7. 7.
    Rafiee J, Arvani F, Harifi A, et al. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 2007, 21(4): 1746–1754CrossRefGoogle Scholar
  8. 8.
    Sanchez R, Arpi A, Minchala L. Fault identification and classification of spur gearbox with feed forward back propagation artificial neural network. In: Proceedings of the 2012 Andean Region International Conference. Washington, D.C.: IEEE, 2012, 215CrossRefGoogle Scholar
  9. 9.
    Barakat M, Lefebvre D, Khalil M, et al. Parameter selection algorithm with self-adaptive growing neural network classifier for diagnosis issues. International Journal of Machine Learning and Cybernetics, 2013, 4(3): 217–233CrossRefGoogle Scholar
  10. 10.
    Yang B S, Han T, An J L. ART-KOHONEN neural network for fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2004, 18(3): 645–657CrossRefGoogle Scholar
  11. 11.
    Jiang Z, Fu H, Li L. Support vector machine for mechanical faults classification. Journal of Zhejiang University SCIENCE A, 2005, 6 (5): 433–439CrossRefGoogle Scholar
  12. 12.
    Jiao B, Xu Z. Multi-classification LSSVM application in fault diagnosis of wind power gearbox. In: Zhang T, ed. Mechanical Engineering and Technology. Berlin: Springer, 2012, 125: 277–283CrossRefGoogle Scholar
  13. 13.
    Kang Y, Wang C, Chang Y. Gear fault diagnosis in time domains by using Bayesian networks. In: Melin P, Castillo O, Ramirez E, et al., eds. Analysis and Design of Intelligent Systems using Soft Computing Techniques. Berlin: Springer, 2007, 41: 618–627CrossRefGoogle Scholar
  14. 14.
    Breiman L, Friedman J, Olshen R, et al. Classification and regression trees. The Wadsworth and Brooks-Cole statisticsprobability series. Boca Raton: Chapman & Hall, 1984Google Scholar
  15. 15.
    Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32zbMATHCrossRefGoogle Scholar
  16. 16.
    Criminisi A, Shotton J. Classification forests. In: Criminisi A, Shotton J, eds. Decision Forests for Computer Vision and Medical Image Analysis. London: Springer, 2013, 25–45CrossRefGoogle Scholar
  17. 17.
    Han X, Yang B S, Lee S J. Application of random forest algorithm in machine fault diagnosis. In: Mathew J, Kennedy J, Ma L, et al., eds. Engineering Asset Management. London: Springer, 2006, 779–784CrossRefGoogle Scholar
  18. 18.
    Yang B S, Di X, Han T. Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 2008, 22 (9): 1716–1725CrossRefGoogle Scholar
  19. 19.
    Karabadji N, Khelf I, Seridi H, et al. Genetic optimization of decision tree choice for fault diagnosis in an industrial ventilator. In: Fakhfakh T, Bartelmus W, Chaari F, et al., eds. Condition Monitoring of Machinery in Non-Stationary Operations. Berlin: Springer, 2012, 277–283CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Diego Cabrera
    • 1
    • 2
    Email author
  • Fernando Sancho
    • 2
  • René-Vinicio Sánchez
    • 1
  • Grover Zurita
    • 1
    • 3
  • Mariela Cerrada
    • 1
    • 4
  • Chuan Li
    • 1
    • 5
  • Rafael E. Vásquez
    • 6
  1. 1.Departamento de Ingeniería MecánicaUniversidad Politécnica SalesianaCuencaEcuador
  2. 2.Departamento de Ciencias de la Computación e Inteligencia ArtificialUniversidad de SevillaMadridEspaña
  3. 3.Departamento de Ingeniería Electro-MecánicaUniversidad Privada BolivianaCochabambaBolivia
  4. 4.Departamento de Sistemas de ControlUniversidad de Los AndesMéridaVenezuela
  5. 5.Research Center of System Health MaintenanceChongqing Technology and Business UniversityChongqingChina
  6. 6.Facultad de Ingeniería MecánicaUniversidad Pontificia BolivarianaMedellínColombia

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