| Título: | Evolving Neural Network Using Genetic Algorithm for Faults Diagnosis of Urban Rail Vehicle Auxiliary Inverter |
| Autores: |
Yao, Dechen; Beijing Jiaotong University Limin, Jia; Beijing Jiaotong University Jianwei, Yang; Beijing University of Civil Engineering Architecture Changxu, Ji; Beijing Jiaotong University Yong, Qin; Beijing Jiaotong University |
| Fecha: | 2013-07-22 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | In this article, an efficient method is proposed to diagnose urban rail vehicle auxiliary inverter faults based on wavelet packet neural network and genetic algorithm. Firstly, the original signals are decomposed into different frequency subbands by wavelet packet. Secondly, the wavelet packet energy eigenvector is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train neural network, In order to improve the function approximation accuracy and general capability of the neural network system, an efficient genetic algorithm approach is used to adjust the parameters of translation and weights functions. The experiment shows that the GA-ANN model gives superior result. This approach can be used as a useful tool for the auxiliary inverter fault diagnosis. |
| Idioma: | Inglés |