Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes a...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/dcdde715913a4975990c9ccf80a90ee0 |
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| Sumario: | For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method. |
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