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

doi:10.1007/s11465-015-0348-8

Front. Mech. Eng.

2015, Vol. 10

Issue (3) : 277-286 https://doi.org/10.1007/s11465-015-0348-8

RESEARCH ARTICLE

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

Diego CABRERA^1,^2,^*(

),Fernando SANCHO²,René-Vinicio SÁNCHEZ¹,Grover ZURITA^1,³,Mariela CERRADA^1,⁴,Chuan LI^1,⁵,Rafael E. VÁSQUEZ⁶

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

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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.

Keywords fault diagnosis spur gearbox wavelet packet decomposition random forest

Corresponding Author(s): Diego CABRERA

Online First Date: 09 September 2015 Issue Date: 23 September 2015

Cite this article:

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ, et al. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition[J]. Front. Mech. Eng., 2015, 10(3): 277-286.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-015-0348-8
https://academic.hep.com.cn/fme/EN/Y2015/V10/I3/277

Fig.1 Training process for the RF classifier

Fig.2 Test process for the RF classifier

Fig.3 Configuration of system for failure’s simulation

Fig.4 Feature extraction process. (a) WPD; (b) energy extraction and features vector building

Tab.1 The best selected wavelets and the best model parameters

Fig.5 Curves of training: (a) oob-error for each set of wavelets with variable number of random features; (b) oob-error for the set of the best wavelets for maximum randomness (1), maximum correlation (256) and perfect randomness (17) with a variable number of trees

Fig.6 Selection of features: (a) Importance of features without selection; (b) importance of features with selection; (c) curve of further training to the selection of features

Tab.2 Confusion matrix

Tab.3 Classifier’s performance measures

Tab.4 AUC individual unit: %

Fig.7 Separation of samples. (a) Location of samples; (b) coordinates eigen-values

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