2015年 第10卷 第3期
《机械工程前沿（英文）》 >> 2015年 第10卷 第3期 doi: 10.1007/s11465-015-0348-8
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
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
下一篇 上一篇
摘要
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.
京公网安备 11010502051620号
