GA-SVR based bearing condition degradation prediction

摘要：

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.

关键词： Genetic Algorithm Support Vector Regression Condition Prediction

作者: FENG Fu-zhou ZHU Dong-dong JIANG Peng-cheng JIANG Hao

作者单位: Department of Mechanical Engineering,Academy of Amoured Force Engineering,Beijing 100072,Peoples Republic of China

会议类型: 国际会议

会议名称: The 8th International Conference on Damage Assessment of Structures(DAMAS 2009)(第八届结构损伤评价国际学术会议)

会议地点: 北京

会议语种:英文

页码: 431-437

在线出版日期: 2009-08-03（万方平台首次上网日期，不代表论文的发表时间）