基于QAR数据的飞机燃油流量预测
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摘要
以目前国内服役最多的B737NG飞机所使用的CFM56-7发动机为研究对象,对飞机快速存取记录器(QAR)的大量数据进行译码分析,以平稳小波Rigorous SURE的方法对数据进行预处理、滤波去噪,分别对发动机N1转速、N2转速、排气温度EGT等参数进行线性或非线性回归分析,结合飞行阶段进行合理划分与建模,分析研究出飞机各主要性能参数与燃油流量(FF)的关系,建立FF的全航程预测模型。通过MATLAB-Simulink进行仿真分析,并选取长航班、中短途航班及复飞航班等5种具有代表性的情况验证FF预测模型与实际流量的误差均在允许范围内,证明所建模型的合理性和普适性。
At present, most of the domestic service B737 NG aircraft using CFM56-7 type engine. As the research object, by decoding the vast data of quick access recorder(QAR), the denoising method with stationary wavelet Rigorous SURE, analyzing the engine speed, N1 speed, N2 speed, exhaust gas temperature(EGT)and other parameters, processing linear or nonlinear regression analysis, combined with the flight phase rational division and modeling, analysis of the main performance parameters and aircraft fuel flow(FF)relationship, full range of the FF prediction model is established. Through MATLAB-Simulink simulation analysis, 5 representative cases of the long flight, short-middle flight, take off-go around flight and complex flight are selected, and errors of the FF prediction model and the actual flow are compared to prove that the model is reasonable and universal.
At present, most of the domestic service B737 NG aircraft using CFM56-7 type engine. As the research object, by decoding the vast data of quick access recorder(QAR), the denoising method with stationary wavelet Rigorous SURE, analyzing the engine speed, N1 speed, N2 speed, exhaust gas temperature(EGT)and other parameters, processing linear or nonlinear regression analysis, combined with the flight phase rational division and modeling, analysis of the main performance parameters and aircraft fuel flow(FF)relationship, full range of the FF prediction model is established. Through MATLAB-Simulink simulation analysis, 5 representative cases of the long flight, short-middle flight, take off-go around flight and complex flight are selected, and errors of the FF prediction model and the actual flow are compared to prove that the model is reasonable and universal.
引文
[1]王少萍.大型飞机机载系统预测与健康管理关键技术[J].航空学报,2014,35(6):1459-1472.Wang S P. Prognostics and health management key technology of aircraft airborne system[J]. Acta Aeronautica et Astronautica Sinica,2014, 35(6):1459-1472.
[2]刘清贵.直面高油价的挑战一中国民航节油中的问题和建议[J].中国民用航空,2005,11(8):25-28.Liu Q G. Face the Challenges of High Oil Price[J]. China Civil Aviation,2005,11(8):25-28.
[3]张金柱,张榕.发动机燃油流量研究[J].山东航空公司工程技术公司,2013.
[4]曹惠玲,贾超.基于QAR的民航发动机燃油流量控制规律研究[J].科学技术与工程,2013,13(13):3814-3817+3827.Cao H L, Jia C. Research on Fuel Flow Control Law of Civil Aviation Engine Based on QAR[J]. Science Technology and Engineering,2013,13(13):3814-3817+3827.
[5]谷润平,黄磊,赵向领.基于QAR数据的飞机发动机性能异常检测[J].航空计算技术,2015,45(04):1-3+7.GU R P, Huang L, ZHAO X L. Detecting Anomalies of Aircraft Engine Performance Based on QAR Data[J]. Aeronautical Computing Technique, 2015,45(04):1-3+7.
[6]耿宏,揭俊.基于QAR数据的飞机巡航段燃油流量回归模型[J].航空发动机,2008,34(04):46-50.Geng H, Jie J. Fuel Flow Regression Model of Aircraft Cruise Based On QAR Data[J]. Aeroengine, 2008,34(04):46-50.
[7]肖冠平、陈静杰,基于QAR数据的民航飞机侧滑角估算方法[J].电光与控制,2015,22(03):86-89.Xiao G P, Chen J J. A Method for Sideslip Angle Estimation of Civil Aircraft Based on QAR Data[J], Electronics Optics&Control, 2015,22(03):86-89.
[8]高飞鹏、黄加阳、陈新霞,基于航后QAR数据译码的APU故障诊断技术[J].计算机测量与控制,2016,24(01):42-45.Gao F P, Huang J Y, Chen X X. Study of Post Flight QAR Data Decoding in Fault Diagnosis of APU[J]. Computer Measurement&Control, 2016,24(01):42-45.
[9]高扬,王向章.基于快速存取记录仪数据的航空发动机整机性能综合评估研究[J].科学技术与工程,2016,16(25):322-326.Gao Y, Wang X Z. Research on Performance Assessment of Overall Aero-engine Based on QAR Data[J]. Science Technology and Engineering, 2016,16(25):322-326.
[10]李书明,任沛,黄燕晓.航空发动机基线方程的拟合[J].机械工程与自动化,2016,12(01):153-154+157.Li S M, Ren P, Huang Y X. Baseline Equation Fitting of Aeroengine[J]. Mechanical Engineering&Automation, 2016, 12(01):153-154+157.
[11]刘永建.基于改进神经网络的民机发动机故障诊断与性能预测研究[D].南京航空航天大学,2012.
[12]吴瑞.航空发动机状态预测与健康管理中的气路数据挖掘方法研究[D].中国民用航空飞行学院,2015,5.
[13]刘洋.基于关联规则挖掘的PW4077D发动机放气活门控制规律研究[J].科技传播2011,23(16):238-239.Liu Y. Research on Control Law of Exhaust Valve of PW4077D Engine Based on Association Rule Mining[J]. Public Communication of Science&Technology, 2011,23(16):238-239.
[14]刘婧.基于飞行数据分析的飞机燃油估计模型[D].南京航空航天大学,2010.
[15]何运成,刘坤,沈笑云,等.飞机燃油消耗估计模型仿真研究[J].计算机仿真,2015,32(05):33-36.He Y C, Liu K, Shen X Y, et al. Simulation study of aircraft fuel consumption estimates model. Computer Simulation[J]. 2015,32(05):33-36.
[16]齐敏,黄世震.基于Matlab的小波去噪算法研究[J].电子器件,2012,35(1):103-106.Qi M, Huang S Z. Research on Wavelet Threshold Denoising Method Based on Matlab[J]. Chinese Journal of Electron Devices, 2012,35(1):103-106.
[17]蔡艳平,李艾华,胡重庆,等.平稳小波自适应去噪用于曲轴瞬时角加速度测量[J].振动、测试与诊断,2010,30(03):310-314+342.Cai Y P, Li A H, Hu C Q, et al. Measurement of Instantaneous Angular Acceleration of Crankshaft Using Adaptive Stationary Wavelet Denoising[J]. Journal of Vibration, Measurement&Diagnosis, 2010,30(03):310-314+342.
[18]王伟,宁东方,张锦.基于能量状态法的飞机节油轨迹优化及其遗传算法实现[J].测控技术,2006(01):56-58+65.Wang W, Ning D F, Zhang J. A Genetic Algorithm for the Trajectory Optimization of fuel Optimal Flight Based on Energy-State[J].Control Measurement&Control Technology, 2006(01):56-58+65.
[19]杨恢先,王绪四,谢鹏鹤,等.改进阈值与尺度间相关的小波红外图像去噪[J].自动化学报,2011,37(10):1167-1174.Yang H X, Wang X S, Xie P H, et al. Infrared Image Denoising Based on Improved Threshold and Inter-scale Correlations of Wavelet Transform[J]. Acta Automatica Sinica, 2011,37(10):1167-1174.
[20]陈晓曦,王延杰,刘恋.小波阈值去噪法的深入研究[J].激光与红外,2012,42(01):105-110.Chen X X, Wang Y J, Liu L. Deep study on wavelet threshold method for image noise removing[J]. Laser&Infrared, 2012, 42(01):105-110.
[21]彭玉华.一种改进的小波变换阈值去噪方法[J].通信学报,2004,23(08):119-123.Peng Y H. An improved thresholding method in wavelet thansform domain for denosing[J]. Journal of China Institute of Communications, 2004,23(08):119-123.
[22] Trani A A. Enhancements to SIMMOD:A Neural Network Post-processor to Estimate Aircraft Fuel Consumption. Virginia Polytechnic Institute and State University,1997.
[23] Yashovardhan S. Chati, Hamsa Balakrishnan:A Gaussian Process Regression Approach to Model Aircraft Engine Fuel Flow Rate.Massachuse.s Institute of Technology, Proceedings of the 8th ACM/IEEE International Conference on Cyber-Physical Systems,Pi.sburgh, PA USA, April 2017(ICCPS 2017).
[24] Hao Long, Xinmin Wang:Aircraft fuel system diagnostic fault detection through expert system, Proceedings of the 7th World Congress on Intelligent Control and Automation June 25-7, 2008,Chongqing, China.
[2]刘清贵.直面高油价的挑战一中国民航节油中的问题和建议[J].中国民用航空,2005,11(8):25-28.Liu Q G. Face the Challenges of High Oil Price[J]. China Civil Aviation,2005,11(8):25-28.
[3]张金柱,张榕.发动机燃油流量研究[J].山东航空公司工程技术公司,2013.
[4]曹惠玲,贾超.基于QAR的民航发动机燃油流量控制规律研究[J].科学技术与工程,2013,13(13):3814-3817+3827.Cao H L, Jia C. Research on Fuel Flow Control Law of Civil Aviation Engine Based on QAR[J]. Science Technology and Engineering,2013,13(13):3814-3817+3827.
[5]谷润平,黄磊,赵向领.基于QAR数据的飞机发动机性能异常检测[J].航空计算技术,2015,45(04):1-3+7.GU R P, Huang L, ZHAO X L. Detecting Anomalies of Aircraft Engine Performance Based on QAR Data[J]. Aeronautical Computing Technique, 2015,45(04):1-3+7.
[6]耿宏,揭俊.基于QAR数据的飞机巡航段燃油流量回归模型[J].航空发动机,2008,34(04):46-50.Geng H, Jie J. Fuel Flow Regression Model of Aircraft Cruise Based On QAR Data[J]. Aeroengine, 2008,34(04):46-50.
[7]肖冠平、陈静杰,基于QAR数据的民航飞机侧滑角估算方法[J].电光与控制,2015,22(03):86-89.Xiao G P, Chen J J. A Method for Sideslip Angle Estimation of Civil Aircraft Based on QAR Data[J], Electronics Optics&Control, 2015,22(03):86-89.
[8]高飞鹏、黄加阳、陈新霞,基于航后QAR数据译码的APU故障诊断技术[J].计算机测量与控制,2016,24(01):42-45.Gao F P, Huang J Y, Chen X X. Study of Post Flight QAR Data Decoding in Fault Diagnosis of APU[J]. Computer Measurement&Control, 2016,24(01):42-45.
[9]高扬,王向章.基于快速存取记录仪数据的航空发动机整机性能综合评估研究[J].科学技术与工程,2016,16(25):322-326.Gao Y, Wang X Z. Research on Performance Assessment of Overall Aero-engine Based on QAR Data[J]. Science Technology and Engineering, 2016,16(25):322-326.
[10]李书明,任沛,黄燕晓.航空发动机基线方程的拟合[J].机械工程与自动化,2016,12(01):153-154+157.Li S M, Ren P, Huang Y X. Baseline Equation Fitting of Aeroengine[J]. Mechanical Engineering&Automation, 2016, 12(01):153-154+157.
[11]刘永建.基于改进神经网络的民机发动机故障诊断与性能预测研究[D].南京航空航天大学,2012.
[12]吴瑞.航空发动机状态预测与健康管理中的气路数据挖掘方法研究[D].中国民用航空飞行学院,2015,5.
[13]刘洋.基于关联规则挖掘的PW4077D发动机放气活门控制规律研究[J].科技传播2011,23(16):238-239.Liu Y. Research on Control Law of Exhaust Valve of PW4077D Engine Based on Association Rule Mining[J]. Public Communication of Science&Technology, 2011,23(16):238-239.
[14]刘婧.基于飞行数据分析的飞机燃油估计模型[D].南京航空航天大学,2010.
[15]何运成,刘坤,沈笑云,等.飞机燃油消耗估计模型仿真研究[J].计算机仿真,2015,32(05):33-36.He Y C, Liu K, Shen X Y, et al. Simulation study of aircraft fuel consumption estimates model. Computer Simulation[J]. 2015,32(05):33-36.
[16]齐敏,黄世震.基于Matlab的小波去噪算法研究[J].电子器件,2012,35(1):103-106.Qi M, Huang S Z. Research on Wavelet Threshold Denoising Method Based on Matlab[J]. Chinese Journal of Electron Devices, 2012,35(1):103-106.
[17]蔡艳平,李艾华,胡重庆,等.平稳小波自适应去噪用于曲轴瞬时角加速度测量[J].振动、测试与诊断,2010,30(03):310-314+342.Cai Y P, Li A H, Hu C Q, et al. Measurement of Instantaneous Angular Acceleration of Crankshaft Using Adaptive Stationary Wavelet Denoising[J]. Journal of Vibration, Measurement&Diagnosis, 2010,30(03):310-314+342.
[18]王伟,宁东方,张锦.基于能量状态法的飞机节油轨迹优化及其遗传算法实现[J].测控技术,2006(01):56-58+65.Wang W, Ning D F, Zhang J. A Genetic Algorithm for the Trajectory Optimization of fuel Optimal Flight Based on Energy-State[J].Control Measurement&Control Technology, 2006(01):56-58+65.
[19]杨恢先,王绪四,谢鹏鹤,等.改进阈值与尺度间相关的小波红外图像去噪[J].自动化学报,2011,37(10):1167-1174.Yang H X, Wang X S, Xie P H, et al. Infrared Image Denoising Based on Improved Threshold and Inter-scale Correlations of Wavelet Transform[J]. Acta Automatica Sinica, 2011,37(10):1167-1174.
[20]陈晓曦,王延杰,刘恋.小波阈值去噪法的深入研究[J].激光与红外,2012,42(01):105-110.Chen X X, Wang Y J, Liu L. Deep study on wavelet threshold method for image noise removing[J]. Laser&Infrared, 2012, 42(01):105-110.
[21]彭玉华.一种改进的小波变换阈值去噪方法[J].通信学报,2004,23(08):119-123.Peng Y H. An improved thresholding method in wavelet thansform domain for denosing[J]. Journal of China Institute of Communications, 2004,23(08):119-123.
[22] Trani A A. Enhancements to SIMMOD:A Neural Network Post-processor to Estimate Aircraft Fuel Consumption. Virginia Polytechnic Institute and State University,1997.
[23] Yashovardhan S. Chati, Hamsa Balakrishnan:A Gaussian Process Regression Approach to Model Aircraft Engine Fuel Flow Rate.Massachuse.s Institute of Technology, Proceedings of the 8th ACM/IEEE International Conference on Cyber-Physical Systems,Pi.sburgh, PA USA, April 2017(ICCPS 2017).
[24] Hao Long, Xinmin Wang:Aircraft fuel system diagnostic fault detection through expert system, Proceedings of the 7th World Congress on Intelligent Control and Automation June 25-7, 2008,Chongqing, China.