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

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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.

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Authors and Affiliations

  1. Departamento de Ingeniería Mecánica, Universidad Politécnica Salesiana, Cuenca, Ecuador

    Diego Cabrera, René-Vinicio Sánchez, Grover Zurita, Mariela Cerrada & Chuan Li

  2. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, Madrid, España

    Diego Cabrera & Fernando Sancho

  3. Departamento de Ingeniería Electro-Mecánica, Universidad Privada Boliviana, Cochabamba, Bolivia

    Grover Zurita

  4. Departamento de Sistemas de Control, Universidad de Los Andes, Mérida, Venezuela

    Mariela Cerrada

  5. Research Center of System Health Maintenance, Chongqing Technology and Business University, Chongqing, 400067, China

    Chuan Li

  6. Facultad de Ingeniería Mecánica, Universidad Pontificia Bolivariana, Medellín, Colombia

    Rafael E. Vásquez

Authors
  1. Diego Cabrera
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  2. Fernando Sancho
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  3. René-Vinicio Sánchez
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  4. Grover Zurita
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  5. Mariela Cerrada
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  6. Chuan Li
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  7. Rafael E. Vásquez
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Corresponding author

Correspondence to Diego Cabrera.

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Cabrera, D., Sancho, F., Sánchez, RV. et al. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition. Front. Mech. Eng. 10, 277–286 (2015). https://doi.org/10.1007/s11465-015-0348-8

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  • DOI: https://doi.org/10.1007/s11465-015-0348-8

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