GA-SVR Based Bearing Condition Degradation Prediction

• Published in: Key engineering materials 2009-01, Vol.413-414, p.431-437
• Main Authors: Jiang, Hao, Feng, Fu Zhou, Zhu, Dong Dong, Jiang, Peng Cheng
• Format: Article
• Language: English
• ISSN: 1013-9826; 1662-9795; 1662-9795
• DOI: 10.4028/www.scientific.net/KEM.413-414.431

A genetic algorithm-support vector regression model (GA-SVR) is proposed for machine performance degradation prediction. The main idea of the method is firstly to select the condition-sensitive features extracted from rolling bearing vibration signals using Genetic Algorithm to form a condition vector. Then prediction model is established for each feature time series. And the third step is to establish support vector regression models to obtain prediction result in each series. Finally, the condition prognosis can be obtained through combing all components to form a condition vector. Vibration data from a rolling bearing bench test process are used to verify accuracy of the proposed method. The results show that the model is an effective prediction method with a higher speed and a better accuracy.