Research on Intelligent Diagnosis Method of Rolling Bearing Fault Based on Machine Learning
| Autor: | Sun Jianyan, Li Lin, Zeng Weijia |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IOP Conference Series: Materials Science and Engineering. 631:032003 |
| ISSN: | 1757-899X 1757-8981 |
| DOI: | 10.1088/1757-899x/631/3/032003 |
| Popis: | In order to solve the problem of poor intelligent diagnosis results of bearing faults, an intelligent diagnosis method of rolling bearing faults based on machine learning is proposed. Through extracting the characteristics of common fault parameters of rolling bearings, the comparative values of bearing fault diagnosis are obtained. In order to ensure the stable operation of the bearings, a machine-learning rolling bearing diagnosis platform is designed for the acceptable deviation values in the standard operating parameters of the bearings, so as to carry out fault diagnosis and feedback control processing on the operating parameters of the bearings in time, thus realizing intelligent diagnosis of rolling bearings faults. Finally, the experiment proves that the intelligent diagnosis method of rolling bearing fault based on machine learning is obviously improved compared with the traditional method. |
| Databáze: | OpenAIRE |
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