RESEARCH ARTICLE

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

  • Diego CABRERA , 1,2 ,
  • Fernando SANCHO 2 ,
  • René-Vinicio SÁNCHEZ 1 ,
  • Grover ZURITA 1,3 ,
  • Mariela CERRADA 1,4 ,
  • Chuan LI 1,5 ,
  • Rafael E. VÁSQUEZ 6
Expand
  • 1. Departamento de Ingeniería Mecánica, Universidad Politécnica Salesiana, Cuenca, Ecuador
  • 2. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, España
  • 3. Departamento de Ingeniería Electro-Mecánica, Universidad Privada Boliviana, Cochabamba, Bolivia
  • 4. Departamento de Sistemas de Control, Universidad de Los Andes, Mérida, Venezuela
  • 5. Research Center of System Health Maintenance, Chongqing Technology and Business University, Chongqing 400067, China
  • 6. Facultad de Ingeniería Mecánica, Universidad Pontificia Bolivariana, Medellín, Colombia

Received date: 24 Feb 2015

Accepted date: 29 Jul 2015

Published date: 23 Sep 2015

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

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.

Cite this article

Diego CABRERA , Fernando SANCHO , René-Vinicio SÁNCHEZ , Grover ZURITA , Mariela CERRADA , Chuan LI , Rafael E. VÁSQUEZ . Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition[J]. Frontiers of Mechanical Engineering, 2015 , 10(3) : 277 -286 . DOI: 10.1007/s11465-015-0348-8

Acknowledgments

The authors want to express a deep gratitude to the Ministry of Higher Education, Science, Technology and Innovation of the Republic of Ecuador (SENESCYT), for their support with this research work. Fernando Sancho wants to thank to the Prometeo Project from the same institution, and the Project TIC-6064 from Junta de Andalucía (Spain) for their support.
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): 60–68

DOI

2
Abbes M S, Fakhfakh T, Haddar M,  Effect of transmission error on the dynamic behaviour of gearbox housing. International Journal of Advanced Manufacturing Technology, 2007, 34(3–4): 211–218

DOI

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): 239–253

DOI

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, 1059–1064 

5
Rgeai M, Gu F, Ball A,  Gearbox fault detection using spectrum analysis of the drive motor current signal. In: Kiritsis D, Emmanouilidis C, Koronios A, , eds. Engineering Asset Lifecycle Management. London: Springer, 2010, 758–769

DOI

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): 2164–2180

DOI

7
Rafiee J, Arvani F, Harifi A,  Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 2007, 21(4): 1746–1754

DOI

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, 215 

DOI

9
Barakat M, Lefebvre D, Khalil M,  Parameter selection algorithm with self-adaptive growing neural network classifier for diagnosis issues. International Journal of Machine Learning and Cybernetics, 2013, 4(3): 217–233

DOI

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): 645–657

DOI

11
Jiang Z, Fu H, Li L. Support vector machine for mechanical faults classification. Journal of Zhejiang University SCIENCE A, 2005, 6(5): 433–439

DOI

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:  277–283

13
Kang Y, Wang C, Chang Y. Gear fault diagnosis in time domains by using Bayesian networks. In: Melin P, Castillo O, Ramirez E, , eds. Analysis and Design of Intelligent Systems using Soft Computing Techniques. Berlin: Springer, 2007, 41: 618–627

14
Breiman L, Friedman J, Olshen R,  Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability series. Boca Raton: Chapman & Hall, 1984

15
Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

DOI

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, 25–45

DOI

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, , eds. Engineering Asset Management. London: Springer, 2006, 779–784 

DOI

18
Yang B S, Di X, Han T. Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 2008, 22(9): 1716–1725

DOI

19
Karabadji N, Khelf I, Seridi H,  Genetic optimization of decision tree choice for fault diagnosis in an industrial ventilator. In: Fakhfakh T, Bartelmus W, Chaari F, , eds. Condition Monitoring of Machinery in Non-Stationary Operations. Berlin: Springer, 2012, 277–283 

DOI

Outlines

/