基于蒙特卡罗发动机竞争失效的下发仿真模型
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摘要
针对常规的解析法预测发动机下发时间建模过程复杂且不易求解的问题,提出了一种基于蒙特卡罗仿真预测发动机首次下发时间的方法。通过分析发动机机队的历史数据,研究排气温度裕度(EGTM)的衰退规律以及性能衰退超标的寿命分布,统计各主要部件发生首次部件损伤的时间分布,并计算偶然性损伤的发生概率,确定偶然性损伤的时间分布,对性能衰退、部件损伤、偶然性损伤三种失效模式进行竞争分析,建立蒙特卡罗仿真模型,预测发动机的首次下发时间规律。结合该机队提供的实际下发数据,利用Kolmogor-ov-Smirnov检验确定首次下发时间的分布类型,经分析,仿真结果的可靠度误差甚小,在-1%～2%之间,从而验证了该方法的合理性和可行性。
To solve the problem of the complicated modeling process and the slving difficulty when using conventional analytical methods to predict the engine delivery time,a method based on Monte Carlo simulation to predict the first delivery time of the engine was proposed.By analyzing the historical data of the engine fleet,the exhaust gas temperature margin(EGTM)decline rule and the lifetime distribution of performance degradation excess were studied.The time distribution of the first hardware damage occurred of each major hardware was counted,the probability of occurrence of accidental damage was calculated,and the time distribution of accidental damage was determined.Performance degradation,hardware damage and accidental damage were subjected to competitive analysis,and a Monte Carlo simulation model was established to predict the law of time when the engine is first delivered.Combined with the actual data provided by the engine fleet,the KolmogorovSmirnov test was used to determine the distribution type of first delivery time.After analysis,the reliability error of the simulation results was very small,between-1% and 2%,and the rationality and feasibility of the method was verified.
To solve the problem of the complicated modeling process and the slving difficulty when using conventional analytical methods to predict the engine delivery time,a method based on Monte Carlo simulation to predict the first delivery time of the engine was proposed.By analyzing the historical data of the engine fleet,the exhaust gas temperature margin(EGTM)decline rule and the lifetime distribution of performance degradation excess were studied.The time distribution of the first hardware damage occurred of each major hardware was counted,the probability of occurrence of accidental damage was calculated,and the time distribution of accidental damage was determined.Performance degradation,hardware damage and accidental damage were subjected to competitive analysis,and a Monte Carlo simulation model was established to predict the law of time when the engine is first delivered.Combined with the actual data provided by the engine fleet,the KolmogorovSmirnov test was used to determine the distribution type of first delivery time.After analysis,the reliability error of the simulation results was very small,between-1% and 2%,and the rationality and feasibility of the method was verified.
引文
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[24]赵宇.可靠性数据分析[M].北京:国防工业出版社,2011.
[25]夏云云,文尚胜,方方.基于Kolmogorov-Smirnov检验的LED可靠性评估[J].光子学报,2016,45(9):26-31.XIA Yunyun,WEN Shangsheng,FANG Fang.Reliability assessment of LED based on Kolmogorov-Smirnov check[J].Journal of Photonics,2016,45(9):26-31.(in Chinese)
[2]FU Xuyun,ZHONG Shisheng,ZHANG Munan.An improved method for aeroengine residual life prediction[J].Journal of Harbin Institute of Technology,2013,45(5):51-55.
[3]CAO Huiling,WU Zemin,QU Chungang,et al.Life on wing prediction of aeroengine based on CBM strategy[J].China Mechanical Engineering,2015,26(13):1725-1730.
[4]孙见忠,左洪福,刘鹏鹏,等.基于动态线性模型的民航发动机性能状态监控与在翼寿命预测[J].系统工程理论与实践,2013,33(12):3243-3250.ZHAO Jianzhong,ZUO Hongfu,LIU Pengpeng,at al.Amethod of condition monitoring and on-wing life prediction for civil aviation aircraft engine based on dynamic linear model[J].System Engineering Theory and Practice,2013,33(12):3243-3250.(in Chinese)
[5]苏春,黄茁,许映秋.基于遗传算法和蒙特卡洛仿真的设备维修策略优化[J].东南大学学报(自然科学版),2006,36(6):941-945.SU Chun,HUANG Zhuo,XU Yingqiu.Optimization of devicesmaintenance and repair policy based on genetic algorithm and Monte Carlo simulation[J].Journal of Southeast University(Natural Science Edition),2006,36(6):941-945.(in Chinese)
[6]FAZA A.A probabilistic model for estimating the effects of photovoltaic sources on the power systems reliability[J].Reliability Engineering&System Safety,2018,171:67-77.
[7]CABALLE N C,CASTRO I T.Analysis of the reliability and the maintenance cost for finite life cycle systems subject to degradation and shocks[J].Applied Mathematical Modelling,2017,52:731-746.
[8]任淑红.民航发动机性能可靠性评估与在翼寿命预测方法研究[D].南京:南京航空航天大学,2010.REN Shuhong.Research on methods of performance reliability assessments and life on wing prediction for civil aeroengine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2010.(in Chinese)
[9]金光.基于退化的可靠性技术:模型、方法及应用[M].北京:国防工业出版社,2014.
[10]凌丹.威布尔分布模型及其在机械可靠性中的应用研究[D].成都:电子科技大学,2011.LING Dan.Research on Weibull distribution and its application in mechanical reliability engineering[D].Chengdu:University of Electronic Science and Technology,2011.(in Chinese)
[11]NEE A Y C,SONG B,ONG S K.Re-engineering manufacturing for sustainability[M].Singapore:Springer Singapore,2013.
[12]NADARAJAH S,KOTZ S.Strength modeling using Weibull distributions[J].Journal of Mechanical Science&Technology,2008,22(7):1247-1254.
[13]辛龙,周越文,翟颖烨,等.基于Weibull分布的航空装备部件寿命预测研究[J].电光与控制,2014,21(12):102-105.XIN Long,ZHOU Yuewen,QU Yingye,et al.Weibull distribution based lifetime prediction for components of aerial equipments[J].Electro-optic and Control,2014,21(12):102-105.(in Chinese)
[14]杨蓓,许科龙.飞机维修手册中意外损伤任务研究[J].科技创新导报,2015,12(36):191-192.YANG Bei,XU Kelong.Accidental damage task research in aircraft maintenance manual[J].Science&Technology Innovation Review,2015,12(36):191-192.(in Chinese)
[15]傅白白.抽象记忆时间的无记忆性及其在真随机模拟中的应用[J].系统工程,2005,23(9):1-3.FU Baibai.Memory lessness of abstract memory time and its application in true stochastic simulation[J].System Engineering,2005,23(9):1-3.(in Chinese)
[16]雷晓云,张志安.基于蒙特卡罗法的一维弹道修正弹落点精度分析[J].系统仿真学报,2016,28(7):1685-1691.1700.LEI Xiaoyun,ZHANG Zhian.Research of impact point accuracy of one-dimensional trajectory correction projectile based on Monte Carlo[J].Journal of System Simulation,2016,28(7):1685-1691,1700.(in Chinese)
[17]DICKMAN B H,GILMAN M J.Monte Carlo optimization[J].Journal of Optimization Theory&Applications,1989,60(1):149-157.
[18]姚若河,徐新才.基于FPGA的高斯分布随机数的生成[J].华南理工大学学报(自然科学版),2013,41(11):1-7.YAO Ruohe,XU Xincai.FPGA-Based generation of Gaussian distribution random numbers[J].Journal of South China University of Technology(Natural Science E-dition),2013,41(11):1-7.(in Chinese)
[19]伍盛菲.面向蒙特卡罗计算的随机数生成FPGA加速器的研究与实现[D].上海:上海交通大学,2013.WU Shengfei.The research and implementation of random numbers accelerator based FPGA for Monte Carlo[D].Shanghai:Shanghai Jiaotong University,2013.(in Chinese)
[20]杨振海,张国志.随机数生成[J].数理统计与管理,2006,25(2):244-252.YANG Zhenhai,ZHANG Guozhi.Generating random variables[J].Mathematical Statistics and Management,2006,25(2):244-252.(in Chinese)
[21]王淑华.逆高斯分布输入下的Integrate-and-Fire模型[D].湖南:湖南师范大学,2008.WANG Shuhua.Integrate-and-Fire model with inputs on inverse Gaussian distribution[D].Hunan:Hunan Normal University,2008.(in Chinese)
[22]MICHAEL J R.Generating random variates using transformations with multiple roots[J].American Statistician,1976,30(2):88-90.
[23]翟亚利,张志华,邵松世.退化产品失效规律分析及其应用[J].系统工程与电子技术,2017,39(6):1420-1424.ZHAI Yali,ZHANG Zhihua,SHAO Songshi.Failure rule analysis of degradation products and its application[J].System Engineering and Electronics,2017,39(6):1420-1424.(in Chinese)
[24]赵宇.可靠性数据分析[M].北京:国防工业出版社,2011.
[25]夏云云,文尚胜,方方.基于Kolmogorov-Smirnov检验的LED可靠性评估[J].光子学报,2016,45(9):26-31.XIA Yunyun,WEN Shangsheng,FANG Fang.Reliability assessment of LED based on Kolmogorov-Smirnov check[J].Journal of Photonics,2016,45(9):26-31.(in Chinese)