【摘要】 排气温度是最能反映航空发动机运行状态的性能参数之一.对连续飞行班次的起飞排气温度裕度(EGTM,Exhaust Gas Temperature Margin)参数进行预测分析,有助于判知航空发动机将来的工作性能,为预防和排除故障提供充分的时间和决策依据.在依据具有非线性、非平稳特征的起飞EGTM历史监测值序列构建预测模型时,基于奇异值分解滤波算法提出了一种联合径向基函数预测网络(RBFPN,Radial Basis Function Prediction Networks)和函数系数自回归模型(FAR,Functional-coefficient Auto Regressive model)的预测方案,充分发挥RBFPN和FAR在预测EGTM参数值变动趋势成分和随机成分的各自优势,使其互为补充,协同处理.实验结果表明该联合预测方案能够有效抑制RBFPN或FAR单独采用时所呈现出的不足,提高预测性能.更多还原

【Abstract】 Exhaust gas temperature is one of the performance parameters which reflect aeroengines’ running state most efficiently.The prediction analysis of the sequent takeoff exhaust gas temperature margin(EGTM) is helpful to estimate aeroengines’ future working performance,which can offer sufficient time reference and decision-making support for the fault prevention and elimination.When building the prediction model according to the EGTM historical observation sequence which was characterized by nonlinearity and nonstationarity,a solution combining radial basis function prediction networks(RBFPN) and functional coefficient autoregressive model(FAR) was proposed based on the sequence partition with singular value decomposition filtering algorithm.The respective advantages of RBFPN and FAR in modeling the trend element and the random element of EGTM sequence were taken complementally and cooperatively.It is indicated by experimentation that the solution can effectively restrain the shortcomings of separate employment of RBFPN or FAR,and improve the prediction performance.更多还原