For the problems of aeroengines such as complicated health monitoring data, insignificant performance degradation characteristics, lack of effective health index construction method and difficulty in remaining useful life (RUL) prediction, an optimal selection and fusion based on grey theory for multimonitoring parameters and a grey forecasting model with full order time power terms (FOTPGM (1,1)) method are proposed. Firstly, the performance degradation state characterization capability of the highdimensional monitoring physical parameters of the aeroengine are parametrically measured by the grey relational analysis (GRA) method, the linear correlation analysis is applied to optimally select of the monitoring parameters as well. Secondly, the grey approximate correlation degree was utilized to make weighted fusion of the selected features, which establishes the uniform health index (HI) of the aeroengine. Taking the HI failure threshold of the training aeroengines as reference, the HI failure threshold of the test aeroengines was determined by the matching method of sliding WindowEuclidean distance. Finally, FOTPGM (1,1) was adopted to predict the RUL of the test aeroengines. Through the experimental analysis, this method can effectively predict the remaining useful life of the aeroengine and obtain a higher prediction accuracy than the traditional method. This method provides a novel idea and means for realizing the fault prediction and health management of the aeroengines.