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基于特征值和特征向量的测量参数选择

白磊 杨磊 胡骏 黄顺洲 周人治

白磊, 杨磊, 胡骏, 黄顺洲, 周人治. 基于特征值和特征向量的测量参数选择[J]. 航空动力学报, 2019, 34(1): 195-200. doi: 10.13224/j.cnki.jasp.2019.01.022
引用本文: 白磊, 杨磊, 胡骏, 黄顺洲, 周人治. 基于特征值和特征向量的测量参数选择[J]. 航空动力学报, 2019, 34(1): 195-200. doi: 10.13224/j.cnki.jasp.2019.01.022
Selection of measurement parameters based on eigenvalues and eigenvectors[J]. Journal of Aerospace Power, 2019, 34(1): 195-200. doi: 10.13224/j.cnki.jasp.2019.01.022
Citation: Selection of measurement parameters based on eigenvalues and eigenvectors[J]. Journal of Aerospace Power, 2019, 34(1): 195-200. doi: 10.13224/j.cnki.jasp.2019.01.022

基于特征值和特征向量的测量参数选择

doi: 10.13224/j.cnki.jasp.2019.01.022

Selection of measurement parameters based on eigenvalues and eigenvectors

  • 摘要: 研究了航空发动机气路故障诊断中测量参数如何选择的问题。利用发动机故障诊断矩阵,提出了基于特征值和特征向量比较不同测量参数选择系统之间优劣的简易快速算法,该算法可以从几何角度直观地展现整体解空间和解矢量的方向等变化情况。通过一个单轴涡喷发动机测量系统对比案例有效地表明:地面测试系统的最大与最小特征值比为33,机载系统的最大与最小特征值比为1008,在该单轴涡喷发动机气路故障诊断方面,地面测试系统比机载系统明显更有利于气路故障诊断。该算法可用于优化机载发动机测量传感器布局、台架测量系统中测量传感器布局,指导工程中测量参数的选择等。

     

  • [1] URBAN L A.Gas path analysis applied to turbine engine condition monitoring[J].Journal of Aircraft,1973,10(7):400-406.
    [2] URBAN L A.Parameter selection for multiple fault diagnostics of gas turbine engines[R].Journal of Engineering for Power,1975,97(2):225-230.
    [3] DOEL D L.An assessment of weighted-least-squares-based gas path analysis[J].Journal of Engineering for Gas Turbines & Power,1994,116(2):366-373.
    [4] TORELLA G,LOMBARDO G.Utilization of neural networks for gas turbine engines[R].Melbourne,Australia:12th International Symposium on Air Breathing Engines,ISABE95-7023,1995.
    [5] TORELLA G.Expert systems and neural networks for isolation in gas turbines[R].Chattanooga,TN,USA:13th International Symposium on Air Breathing Engines,ISABE 97-7148,1997.
    [6] PALMER C A.Combing bayesian belief networks with gas path analysis for test cell diagnostics and overhaul[R].ASME Paper 98-GT-168,1998.
    [7] ROMESSIS C,MATHIOUDAKIS K.Bayesian network approach for gas path fault diagnosis[J].Journal of Engineering for Gas Turbines & Power,2004,128(1):64-72.
    [8] SURESH S,ANKUSH G.Fault diagnostics using genetic algorithm for advanced cycle gas turbine[R].ASME Paper GT-2002-30021,2002.
    [9] 尉询楷,陆波,汪诚,等.支持向量机在航空发动机故障诊断中的应用[J].航空动力学报,2004,19(6):844-848.WEI Xunkai,LU Bo,WANG Cheng,et al.Applications of support vector machines to aeroengine fault diagnosis[J].Journal of Aerospace Power,2004,19(6):844-848.(in Chinese)
    [10] STAMATIS A,MATHIOUDAKIS K,PAPAILIOU K.Optimal measurement and health index selection for gas turbine performance status and fault diagnosis[J].Journal of General Physiology,1992,114(2):209-216.
    [11] OGAJI S O T,SINGH R.Study of the optimisation of measurement sets for gas path fault diagnosis in gas turbines[R].ASME Paper GT-2002-30050,2002.
    [12] KABOUKOS P,OIKONOMOU P,STAMATIS A,et al.Optimizing diagnostic effectiveness of mixed turbofans by means of adaptive modelling and choice of appropriate monitoring parameters[R].Manchester,UK:The RTP AVT Symposium on “Ageing Mechanisms and Control Part B:Monitoring and Management of Gas Turbine Fleets for Extended Life and Reduced Costs”,2001.
    [13] 唐耿林.航空发动机性能监视参数选择的研究[J].推进技术,1998,19(2):38-42.TANG Genglin.Investigations on selecting performance-monitoring parameters of aeroengine[J].Journal of Propulsion Technology,1998,19(2):38-42.(in Chinese)
    [14] 孙祥逢,陈玉春,胡福.发动机故障诊断主因子模型的测量参数选择[J].航空动力学报,2010,25(1):129-135.SUN Xiangfeng,CHEN Yuchun,HU Fu.Research on selection of measurement parameters of engine fault diagnosis based on primary factor model[J].Journal of Aerospace Power,2010,25(1):129-135.(in Chinese)
    [15] LI Y G,JASMANI M S.Measurement selection for multi-component gas path diagnostics using analytical approach and measurement subset concept[R].ASME Paper GT 2010-22402,2010.
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出版历程
  • 收稿日期:  2018-01-06
  • 刊出日期:  2019-01-28

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