关键词: 燃油泵;参数预测;模糊信息粒化;奇异值分解;极限学习机

Abstract: The performance degradation of airborne fuel pump is nonlinear and multi-stage degradation pattern. In order to improve the prediction accuracy of the fuel pump performance degradation and get an accuracy prediction range, a novel prediction method based on Singular Value Decomposition-fuzzy information granulation and optimized Extreme Learning Machine (ELM) is proposed. Aiming at the defect of the traditional granulation prediction method which directly use the granulation analysis to the original series, Singular Value Decomposition is executed for those degradation index sequences and the trend term and detrend term are obtained. Fuzzy information granulation is executed for the de-trend terms. Then, the trend term and the granulated detrend term are input into the ELM to perform regression prediction. In this process, particle swarm optimization (PSO) is used to optimize ELM parameters. Finally, the prediction model is evaluated according to the comparison between the measured values and the predicted values. Experimental results show that the proposed method can effectively rack the trend and realize the change trend and spatial prediction for the fuel pump performance degradation index.

Key words: Fuel pump;Parameter prediction;Fuzzy information granulation;Singular value decomposition;Extreme learning machine