涡轴发动机自适应混合诊断模型高斯加权聚类方法

佘云峰; 黄金泉; 鲁峰

涡轴发动机自适应混合诊断模型高斯加权聚类方法

南京航空航天大学能源与动力学院,南京 210016

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出版历程
- 收稿日期: 2010-04-26
- 修回日期: 2010-07-04
- 刊出日期: 2011-05-28

Gaussian weighted sum clustering method of adaptive hybrid diagnostic model for turbo-shaft engine

College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

摘要

摘要: 针对整个飞行包线内涡轴发动机健康参数估计问题,提出基于高斯加权聚类的机载自适应混合模型建立方法.机载自适应混合模型由卡尔曼滤波器和神经网络组成,由于飞行数据样本庞大,采用高斯加权模型对涡轴发动机飞行数据进行实时聚类,利用聚类数据更新神经网络权重,并实现自适应混合模型工作范围的自动扩展.仿真结果表明了该方法的有效性,采用高斯加权聚类的自适应混合模型提高了全包线内性能跟踪的精度.
- 涡轴发动机 /
- 高斯加权模型 /
- 机载自适应混合模型 /
- 性能参数估计 /
- 卡尔曼滤波 /
- 神经网络
Abstract: A Gaussian weighted sum clustering method of on-board adaptive hybrid model was proposed for health parameter estimation of turbo-shaft engine in the full flight envelope.The on-board adaptive hybrid model was composed of Kalman filter and neural network.Gaussian weighted sum model was used to cluster a huge amount of flight data in real-time and update the neural network weighting.The working range of adaptive hybrid model was increased automatically with this method.The simulation results show the validity of the proposed method.Adaptive hybrid model based on Gaussian weighted sum clustering increases the accuracy for turbo-shaft engine performance tracking in the full flight envelope.
- turbo-shaft engine /
- Gaussian weighted sum model /
- on-board adaptive hybrid model /
- performance parameter estimation /
- Kalman filter /
- neural network

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参考文献(12)

[1]	Ganguli R.Application of fuzzy logic for fault isolation of jet engines .ASME 2001-GT-13,2001.
[2]	赵世荣,黄向华.应用神经网络信息融合诊断航空发动机故障[J].航空动力学报,2008,23(1):163-168. ZHAO Shirong,HUANG Xianghua.Fault diagnosis for aeroengine gas path components based on neural network multisensor data fusion[J].Journal of Aerospace Power,2008,23(1):163-168.(in Chinese)
[3]	Jensen F V.Bayesian networks and decision graphs[M].New York:Springer-Verlag,2001.
[4]	鲁峰,黄金泉.基于遗传算法的航空发动机机载模型支持向量机修正方法[J].航空动力学报,2009,24(4):880-885. LU Feng,HUANG Jinquan.AGA-LSSVR compensation for on-board self-tuning model of aero-engine model[J].Journal of Aerospace Power,2009,24(4):880-885.(in Chinese)
[5]	鲁峰,黄金泉.航空发动机部件性能故障融合诊断方法研究[J].航空动力学报,2009,24(7):1649-1653. LU Feng,HUANG Jinquan.Research on performance fault fusion diagnosis of aero-engine component[J].Journal of Aerospace Power,2009,24(7):1649-1653.(in Chinese)
[6]	Volponi A J,Brotherton T.A bootstrap data methodology is for sequential hybrid engine model building .Big Sky,MT:IEEE Aerospace Conference Proceedings,2005.
[7]	Volponi A J.Enhanced self tuning on-board real-time model (eSTORM) for aircraft engine performance health tracking .NASA/CR-2008-215272,2008.
[8]	Luppold R,Brotherton T,Volponi A.Adaptive on-wing gas turbine engine performance estimation .Big Sky,MT:IEEE Aerospace Conference Proceeding,2007.
[9]	Frith P,Karvounis G.Model-based decision support tools for T700 engine health monitoring .Melbourne,Australia:DSTO International Conference on Health and Usage Monitoring,2001.
[10]	Litt J S,Simon D L.Toward a real-time measurement-based system for estimation of helicopter engine degradation due to compressor erosion .NASA/TM-2007-214843,2007.
[11]	Volponi A J,De Pold H,Ganguli R.The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics:a comparative study[J].Journal of Engineering for Gas Turbine and Power,2003,125(10):917-924.
[12]	黄伟斌, 黄金泉.航空发动机故障诊断的机载自适应模型[J].航空动力学报,2008,23(3):581-584. HUANG Weibin,HUANG Jinquan.On board self-tuning model for aero-engine fault diagnostics[J].Journal of Aerospace Power,2008,23(3):581-584.(in Chinese)