双轴分排涡扇发动机气路故障诊断测量参数选择方法
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摘要
针对地面台架试车气路故障诊断测量参数的优化选择提出了一种四步优化方法.该方法包括测量参数敏感性分析、部件性能参数相关性分析、影响系数矩阵条件数分析和遗传算法检验.第1步选择出了总空气流量;第2步选择出了风扇出口总温和压气机出口总温;第3步选择出了12种对故障诊断有利的测量参数组合;第4步选择出了最有利于故障隔离的测量参数组合.利用遗传算法进行单一故障辨识的结果表明:所选的12种测量参数组合的适应度都大于0.85,接近于最优的适应度1.对于最有利的测量参数组合,利用遗传算法的故障辨识验证结果表明,其适应度均大于0.9,验证了四步优化方法的有效性.
Aiming at measurement parameters optimal selection for gas path fault diagnosis in ground test bed,a four-step optimal method was presented.The four-step optimal method includes measurement parameters sensitivity analysis,component performance parameters correlation analysis,influence coefficient matrix condition number analysis and genetic algorithm validation.According to the first step,total air mass flow was picked out.In the second step,total temperature at fan exit and total temperature at compressor exit were picked out.In the third step,twelve measurement parameters combinations beneficial to fault diagnosis were picked out.In the final step,the best measurement parameters combination was obtained.Based on the genetic algorithm,the simulated diagnostic results show that,with these twelve measurement parameters combinations,all the fitness values for each single fault diagnosis are more than 0.85,which approximate the optimal fitness value 1.As to the most promising measurement parameter combination,all the fitness values are greater than 0.9by genetic algorithm validation,which demonstrate the validity of this four-step optimal method.
Aiming at measurement parameters optimal selection for gas path fault diagnosis in ground test bed,a four-step optimal method was presented.The four-step optimal method includes measurement parameters sensitivity analysis,component performance parameters correlation analysis,influence coefficient matrix condition number analysis and genetic algorithm validation.According to the first step,total air mass flow was picked out.In the second step,total temperature at fan exit and total temperature at compressor exit were picked out.In the third step,twelve measurement parameters combinations beneficial to fault diagnosis were picked out.In the final step,the best measurement parameters combination was obtained.Based on the genetic algorithm,the simulated diagnostic results show that,with these twelve measurement parameters combinations,all the fitness values for each single fault diagnosis are more than 0.85,which approximate the optimal fitness value 1.As to the most promising measurement parameter combination,all the fitness values are greater than 0.9by genetic algorithm validation,which demonstrate the validity of this four-step optimal method.
引文
[1]LI Yiguang.Performance analysis based gas turbine diagnostics:a review[J].Journal of Power and Energy,2002,216(5):363-377.
[2]Stamatis A,Mathioudakis K,Papailiou K.Optimal measurement and health index selection for gas turbine performance status and fault diagnosis[J].Journal of Engineering for Gas Turbines and Power,1992,114(2):209-216.
[3]Provost M J.The use of optimal estimation techniques in the analysis of gas turbine[D].Cranfield,UK:Cranfield University,1994.
[4]Kaboukos P,Oikonomou P,Stamatis A,et al.Optimizing diagnostic effectiveness of mixed turbofans by means of adaptive modeling and choice of appropriate monitoring parameters[R].Manchester,UK:Defense Technical Information Center Compilation Part Notice ADP014126,2001.
[5]唐耿林.航空发动机性能监视参数选的研究[J].推进技术,1998,19(2):92-96.TANG Genglin.Investigations on selecting performancemonitoring parameters of aero-engine[J].Journal of Propulsion Technology,1998,19(2):92-96.(in Chinese)
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[10]Pinelli M,Spina P.Gas turbine field performance determination:the source of some uncertainties[J].Journal of Engineering for Gas Turbine and Power,2002,124(1):155-160.
[11]Aretakis N,Mathioudakis K,Stamatis A.Non-linear engine component fault diagnosis from a limited number of measurements using a combinatorial approach[J].Journal of Engineering for Gas Turbine and Power,2010,132(4):041701.1-041701.9.
[12]Jasmani M,LI Yiguang,Ariffin Z.Measurement selection for component gas path diagnostics using analytical approach and measurement subset concept[J].Journal of Engineering for Gas Turbine and Power,2011,133(11):111701.1-111701.9.
[13]Ogaji S,Sampath S,Singh R.Parameter selection for diagnosing agas turbines performance deterioration[J].Applied Energy,2002,73(1):25-46.
[14]LI Yiguang.An genetic algorithm approach to estimate performance status of gas turbine[J].ASME Paper GT2008-50175,2008.
[15]LI Yiguang,Pilidis P.GA-based design-point performance adaptation and its comparison with ICM-based approach[J].Applied Energy,2010,87(1):340-348.
[16]Zedda M,Singh R.Gas turbine engine and sensor fault diagnosis using optimization techniques[R].AIAA 99-2530,1999.
[17]Gulati A,Zedda M,Singh R.Gas turbine engine and sensor multiple operating point analysis using optimization techniques[R].AIAA-2000-3716,2000.
[18]LI Yiguang,Pilidis P,Newby M A.An adaption approach for gas turbine design point performance simulation[J].Journal of Engineering for Gas Turbine and Power,2006,128(4):789-795.
[2]Stamatis A,Mathioudakis K,Papailiou K.Optimal measurement and health index selection for gas turbine performance status and fault diagnosis[J].Journal of Engineering for Gas Turbines and Power,1992,114(2):209-216.
[3]Provost M J.The use of optimal estimation techniques in the analysis of gas turbine[D].Cranfield,UK:Cranfield University,1994.
[4]Kaboukos P,Oikonomou P,Stamatis A,et al.Optimizing diagnostic effectiveness of mixed turbofans by means of adaptive modeling and choice of appropriate monitoring parameters[R].Manchester,UK:Defense Technical Information Center Compilation Part Notice ADP014126,2001.
[5]唐耿林.航空发动机性能监视参数选的研究[J].推进技术,1998,19(2):92-96.TANG Genglin.Investigations on selecting performancemonitoring parameters of aero-engine[J].Journal of Propulsion Technology,1998,19(2):92-96.(in Chinese)
[6]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)
[7]Borguet S,Leonard O.A sparse estimation approach to fault isolation[J].Journal of Engineering for Gas Turbine and Power,2010,132(2):021601.1-021601.7.
[8]范作民,孙春林,白杰.航空发动机故障诊断导论[M].北京:科学出版社,2004.
[9]Litt J S.An optimal orthogonal decomposition method for Kalman filter-based turbofan engine thrust estimation[J].Journal of Engineering for Gas Turbine and Power,2008,130(1):011601.1-011601.12.
[10]Pinelli M,Spina P.Gas turbine field performance determination:the source of some uncertainties[J].Journal of Engineering for Gas Turbine and Power,2002,124(1):155-160.
[11]Aretakis N,Mathioudakis K,Stamatis A.Non-linear engine component fault diagnosis from a limited number of measurements using a combinatorial approach[J].Journal of Engineering for Gas Turbine and Power,2010,132(4):041701.1-041701.9.
[12]Jasmani M,LI Yiguang,Ariffin Z.Measurement selection for component gas path diagnostics using analytical approach and measurement subset concept[J].Journal of Engineering for Gas Turbine and Power,2011,133(11):111701.1-111701.9.
[13]Ogaji S,Sampath S,Singh R.Parameter selection for diagnosing agas turbines performance deterioration[J].Applied Energy,2002,73(1):25-46.
[14]LI Yiguang.An genetic algorithm approach to estimate performance status of gas turbine[J].ASME Paper GT2008-50175,2008.
[15]LI Yiguang,Pilidis P.GA-based design-point performance adaptation and its comparison with ICM-based approach[J].Applied Energy,2010,87(1):340-348.
[16]Zedda M,Singh R.Gas turbine engine and sensor fault diagnosis using optimization techniques[R].AIAA 99-2530,1999.
[17]Gulati A,Zedda M,Singh R.Gas turbine engine and sensor multiple operating point analysis using optimization techniques[R].AIAA-2000-3716,2000.
[18]LI Yiguang,Pilidis P,Newby M A.An adaption approach for gas turbine design point performance simulation[J].Journal of Engineering for Gas Turbine and Power,2006,128(4):789-795.