| Título: | Mechanical Fault Diagnosis Based on LMD-Approximate Entropy and LSSVM |
| Autores: |
Dong, Zengshou; Taiyuan University of Science and Technology Tian, Xueqin; Taiyuan University of Science and Technology Zeng, Jianchao; Taiyuan University of Science and Technology |
| Fecha: | 2013-02-01 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | Mechanical fault diagnosis is a feature extraction and pattern recognition process. The feature extraction is related to the accuracy of fault diagnosis and the reliability of the prediction. How to extract fault features effectively and build an accurate model to recognize fault is the key of diagnosis technology. Accordingly, a feature extraction method based on LMD-approximate entropy was proposed, and combined it with LSSVM to diagnose mechanical fault. Firstly, the decomposition of fault feature by LMD, and then the approximate entropy of product function were taken to extract fault features accurately. Finally, the eigenvectors were input to LS-SVM for fault recognition. Compared with EMD and wavelet decomposition, the results show that it can extract fault features effectively and can improve the accuracy and speed of fault diagnosis. |
| Idioma: | Inglés |