应用支持向量回归机探索发动机VSV调节规律
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摘要
发动机可调静子叶片(VSV)调节规律极其复杂,通过挖掘快速存取记录装置(QAR)数据对VSV调节规律进行了深入研究。首先,通过PW4077D发动机健康状态的QAR数据,建立基于粒子群优化(PSO)算法的支持向量回归机(SVR)模型,来探索VSV调节规律;然后,利用后续航班数据对PSO-SVR模型进行验证,并将验证结果与传统的PSO-BP神经网络模型进行对比;最后,应用PSO-SVR模型进行发动机故障诊断。研究结果表明:PSOSVR模型的回归预测精度优于PSO-BP神经网络模型,能够准确反映VSV的调节规律。可将其用于发动机的状态监控和故障诊断,亦可为VSV控制系统设计提供参考。
The engine variable stator vane( VSV) regulation law is extremely complex,and through mining quick access recorder( QAR) data,the VSV regulation law is studied. Firstly,the support vector regression( SVR) model based on particle swarm optimization( PSO) is established through the QAR data of PW4077 D engine health condition to explore the regulation law of VSV. Then,the PSO-SVR model is validated by the subsequent flight data,and the verification results are compared with the traditional PSO-BP neural network model. Finally,the PSO-SVR model is applied to engine fault diagnosis. The results show that the regression prediction accuracy of the PSO-SVR model is better than that of the PSO-BP neural network model,and it can accurately reflect the VSV regulation rule. It can be used in the condition monitoring and fault diagnosis of engine,and can also provide reference for the design of VSV control system.
The engine variable stator vane( VSV) regulation law is extremely complex,and through mining quick access recorder( QAR) data,the VSV regulation law is studied. Firstly,the support vector regression( SVR) model based on particle swarm optimization( PSO) is established through the QAR data of PW4077 D engine health condition to explore the regulation law of VSV. Then,the PSO-SVR model is validated by the subsequent flight data,and the verification results are compared with the traditional PSO-BP neural network model. Finally,the PSO-SVR model is applied to engine fault diagnosis. The results show that the regression prediction accuracy of the PSO-SVR model is better than that of the PSO-BP neural network model,and it can accurately reflect the VSV regulation rule. It can be used in the condition monitoring and fault diagnosis of engine,and can also provide reference for the design of VSV control system.
引文
[1]唐庆如,孔萌.CFM56-7B发动机VSV结构损伤分析[J].航空维修与工程,2011(4):31-33.TANG Q R,KONG M.Analysis of CFM56-7B VSV structural damage[J].Aviation Maintenance Engineering,2011(4):31-33(in Chinese).
[2]李世林.VSV系统对CFM56发动机喘振的影响分析[J].科学技术与工程,2011,11(20):4934-4936.LI S L.Research on VSV faults based CFM56 engine surge[J].Science Technology and Engineering,2011,11(20):4934-4936(in Chinese).
[3]黄爱华.涡扇发动机可调静子叶片控制规律研究[J].燃气涡轮试验与研究,2017,30(1):48-51.HUANG A H.Control law of variable stator vane for turbofan engine[J].Gas Turbine Experiment and Research,2017,30(1):48-51(in Chinese).
[4]吴秀宽,林森.某涡扇发动机风扇进口可调导流叶片调节规律分析[C]∥第五届中国航空学会青年科技论坛文集(第5集).北京:北京航空航天大学出版社,2012:227-231.WU X K,LIN S.The analyse about the control law of turbofan’IGV[C]∥Proceedings of the Fifth China Aviation Society Youth Science and Technology Forum(Fifth Episodes).Beijing:Beihang University Press,2012:227-231(in Chinese).
[5]曹志鹏,刘波,丁伟.静叶角度调节对组合压气机性能优化机理[J].北京航空航天大学学报,2007,33(8):878-881.CAO Z P,LIU B,DING W.Stator setting angles adjustment on performance improvement of axial-centrifugal compressor[J].Journal of Beijing University of Aeronautics and Astronautics,2007,33(8):878-881(in Chinese).
[6]张健,任铭林.静叶角度调节对压气机性能影响的试验研究[J].航空动力学报,2000,15(1):27-30.ZHANG J,REN M L.Experimental investigation on effect of stator vane angle adjustment on compressor performance[J].Journal of Aerospace Power,2000,15(1):27-30(in Chinese).
[7]张宇飞,么子云,唐松林,等.一种基于主成分分析和支持向量机的发动机故障诊断方法[J].中国机械工程,2016,27(24):3307-3311.ZHANG Y F,YAO Z Y,TANG S L,et al.An engine fault diagnosis method based on PCL and SVR[J].China Mechanical Engineering,2016,27(24):3307-3311(in Chinese).
[8]BI F R,LIU Y P.Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine[J].Transactions of Tianjin University,2016,22(6):536-543.
[9]殷锴,钟诗胜,那媛,等.基于BP神经网络的航空发动机故障检测技术研究[J].航空发动机,2017,43(1):53-57.YIN K,ZHONG S S,NA Y,et al.Research on aeroengine fault detection technology based on BP neural network[J].Aeroengine,2017,43(1):53-57(in Chinese).
[10]栾圣罡.基于气路参数样本的航空发动机状态监视方法与系统研究[D].哈尔滨:哈尔滨工业大学,2008.LUAN S G.Aeroengine condition monitoring technique and system based on gas path parameter sample[D].Harbin:Harbin Institute of Technology,2008(in Chinese).
[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:南京航空航天大学,2012.LIU Y J.Research on modified neural network for fault diagnosis and performance prediction of aeroengine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2012(in Chinese).
[12]王芳.基于支持向量机的沪深300指数回归预测[D].济南:山东大学,2015.WANG F.CSI 300 index regression prediction based on support vector machine[D].Jinan:Shandong University,2015(in Chinese).
[13]史峰,王辉,郁磊,等.智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.SHI F,WANG H,YU L,et al.30 cases analysis of intelligent algorithm[M].Beijing:Beihang University Press,2011(in Chinese).
[14]崔智全.民航发动机气路参数偏差值挖掘方法及其应用研究[D].哈尔滨:哈尔滨工业大学,2013.CUI Z Q.Civil aeroengine gas path parameter deviation mining method with application[D].Harbin:Harbin Institute of Technology,2013(in Chinese).
[15]Boeing.777 aircraft maintenance manual[Z].Chicago:Boeing,2015.
[16]彭泽琰,刘刚.航空燃气轮机原理[M].北京:国防工业出版社,2000.PENG Z Y,LIU G.Principles of aviation gas turbines[M].Beijing:National Defense Industry Press,2000(in Chinese).
[17]Pratt&Whitney Company.ECMⅡtraining manual[Z].Hartford:Pratt&Whitney Company,1994.
[18]周百政,曹惠玲.基于EHM软件思路的QAR数据处理[J].航空维修与工程,2010(4):60-62.ZHOU B Z,CAO H L.QAR data processing based on the method of EHM software[J].Aviation Maintenance&Engineering,2010(4):60-62(in Chinese).
[2]李世林.VSV系统对CFM56发动机喘振的影响分析[J].科学技术与工程,2011,11(20):4934-4936.LI S L.Research on VSV faults based CFM56 engine surge[J].Science Technology and Engineering,2011,11(20):4934-4936(in Chinese).
[3]黄爱华.涡扇发动机可调静子叶片控制规律研究[J].燃气涡轮试验与研究,2017,30(1):48-51.HUANG A H.Control law of variable stator vane for turbofan engine[J].Gas Turbine Experiment and Research,2017,30(1):48-51(in Chinese).
[4]吴秀宽,林森.某涡扇发动机风扇进口可调导流叶片调节规律分析[C]∥第五届中国航空学会青年科技论坛文集(第5集).北京:北京航空航天大学出版社,2012:227-231.WU X K,LIN S.The analyse about the control law of turbofan’IGV[C]∥Proceedings of the Fifth China Aviation Society Youth Science and Technology Forum(Fifth Episodes).Beijing:Beihang University Press,2012:227-231(in Chinese).
[5]曹志鹏,刘波,丁伟.静叶角度调节对组合压气机性能优化机理[J].北京航空航天大学学报,2007,33(8):878-881.CAO Z P,LIU B,DING W.Stator setting angles adjustment on performance improvement of axial-centrifugal compressor[J].Journal of Beijing University of Aeronautics and Astronautics,2007,33(8):878-881(in Chinese).
[6]张健,任铭林.静叶角度调节对压气机性能影响的试验研究[J].航空动力学报,2000,15(1):27-30.ZHANG J,REN M L.Experimental investigation on effect of stator vane angle adjustment on compressor performance[J].Journal of Aerospace Power,2000,15(1):27-30(in Chinese).
[7]张宇飞,么子云,唐松林,等.一种基于主成分分析和支持向量机的发动机故障诊断方法[J].中国机械工程,2016,27(24):3307-3311.ZHANG Y F,YAO Z Y,TANG S L,et al.An engine fault diagnosis method based on PCL and SVR[J].China Mechanical Engineering,2016,27(24):3307-3311(in Chinese).
[8]BI F R,LIU Y P.Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine[J].Transactions of Tianjin University,2016,22(6):536-543.
[9]殷锴,钟诗胜,那媛,等.基于BP神经网络的航空发动机故障检测技术研究[J].航空发动机,2017,43(1):53-57.YIN K,ZHONG S S,NA Y,et al.Research on aeroengine fault detection technology based on BP neural network[J].Aeroengine,2017,43(1):53-57(in Chinese).
[10]栾圣罡.基于气路参数样本的航空发动机状态监视方法与系统研究[D].哈尔滨:哈尔滨工业大学,2008.LUAN S G.Aeroengine condition monitoring technique and system based on gas path parameter sample[D].Harbin:Harbin Institute of Technology,2008(in Chinese).
[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:南京航空航天大学,2012.LIU Y J.Research on modified neural network for fault diagnosis and performance prediction of aeroengine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2012(in Chinese).
[12]王芳.基于支持向量机的沪深300指数回归预测[D].济南:山东大学,2015.WANG F.CSI 300 index regression prediction based on support vector machine[D].Jinan:Shandong University,2015(in Chinese).
[13]史峰,王辉,郁磊,等.智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.SHI F,WANG H,YU L,et al.30 cases analysis of intelligent algorithm[M].Beijing:Beihang University Press,2011(in Chinese).
[14]崔智全.民航发动机气路参数偏差值挖掘方法及其应用研究[D].哈尔滨:哈尔滨工业大学,2013.CUI Z Q.Civil aeroengine gas path parameter deviation mining method with application[D].Harbin:Harbin Institute of Technology,2013(in Chinese).
[15]Boeing.777 aircraft maintenance manual[Z].Chicago:Boeing,2015.
[16]彭泽琰,刘刚.航空燃气轮机原理[M].北京:国防工业出版社,2000.PENG Z Y,LIU G.Principles of aviation gas turbines[M].Beijing:National Defense Industry Press,2000(in Chinese).
[17]Pratt&Whitney Company.ECMⅡtraining manual[Z].Hartford:Pratt&Whitney Company,1994.
[18]周百政,曹惠玲.基于EHM软件思路的QAR数据处理[J].航空维修与工程,2010(4):60-62.ZHOU B Z,CAO H L.QAR data processing based on the method of EHM software[J].Aviation Maintenance&Engineering,2010(4):60-62(in Chinese).