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《Proceedings of the Csee》 2001-01
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CHEN Yao wu, WANG Le yu (Dept. Of Instrumentation Sci.& Eng.,Zhejiang University ,Hangzhou 310027,China)  
According to the characteristics of the rotor state feature data time series, this paper presents a novel rotor state forecasting model based on modular neural networks.The forecasting and the fault diagnosis are integrated in the model.With the feature data sample,the fault diagnosis subsystem is first applied to diagnose the rotor state.In accordance with the rotor state,the corresponding forecasting modules based on neural networks are activated to forecast the different feature data time series.In the end,the fault diagnosis subsystem is further applied to diagnose the future rotor state with the forecasted feature data sample.The simulation results show that this model improves greatly the reliability to the rotor state forecasting.The structure of the model and simulation results are discussed in detail.
【Fund】: 中国石化总公司科技发展项目! (395 0 17)
【CateGory Index】: TP18
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