关键词: 航空发动机, 径向基函数（RBF）变权重组合预测, 磨损, 趋势预测

Abstract: The contents of metal in the spectral oil of the aeroengine are affected by many complex factors，so wear trend prediction accuracy is relatively low.To solve this problem，an RBF neural network variable weight combination forecasting model（RBFNN-VWCF） is proposed for wear trend prediction of aircraft engine.As the dimensions of input layer affect the prediction accuracy severely，C-C method that originates from chaos theory is introduced to determine the dimensions of input samples and output samples.BP network and the SVM model are carried on as sub-prediction model to predict the trend of iron，and then put the prediction value of sub-model as input variable of RBFNN-VWCF model，orthogonal least square is used to train the RBFNN-VWCF model，determine the weight of sub-model at different times，at the same time parameters which affecting the prediction accuracy are discussed in this paper.The simulation results show that，RBFNN-VWCF model takes full advantage of effective information of sub-model，and it can be able to reflect the wear trend of engine more objectively.The forecasting result is robust，compared with the single model RBFNN-VWCF model has higher prediction accuracy and strong practical value，provides powerful support for decision-making of the engine next step.

Key words: aeroengine, Radial Basis Function（RBF） variable weight combination forecasting, wear, trend prediction