Artículo
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
Autor/es | Cabrera, Diego
Sancho Caparrini, Fernando Sánchez, René-Vinicio Zurita, Grover Cerrada, Mariela Li, Chuan Vásquez, Rafael E. |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2015 |
Fecha de depósito | 2021-04-15 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Junta de Andalucía |
Identificador del proyecto | TIC-6064 |
Cita | Cabrera, D., Sancho Caparrini, F., Sánchez, R., Zurita, G., Cerrada, M., Li, C. y Vásquez, R.E. (2015). Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition. Frontiers of Mechanical Engineering, 10 (3), 277-286. |
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