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Time Series Forecast of Rotor Vibration Based on Support Vector Autoregressive

WANG Lei,ZHANG Rui-qing(Department of Power Engineering,Shenyang Institute of Engineering,Shenyang 110136,China)  
In order to monitor the rotor vibration of turbine,improve the safety of power plant,a new model,support vector autoregressive(SVAR) is applied to the time series forecast of rotor vibration combining the kernel algorithm of Statistical Learning Theory(SLT),Support Vector Machine(SVM) with AR algorithm.The model based on SVAR is compared with the model based on Gray Model and the model based on Autoregressive by a case.The result indicates that the model based on SVAR has powerful prediction precision and generalization,and it provides a new method to avoid the fault due to the vibration.
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