基于视情维修的机队维修决策方法
|
摘要
飞机结构疲劳寿命预测受不确定性因素影响较大,为了克服这种缺陷,提出了将扩展卡尔曼滤波和实时状态数据相结合的结构剩余寿命预测方法。通过对结构的疲劳裂纹扩展模型中的不确定性参数进行实时更新,使模型具有自适应消除噪声能力,提高了寿命预测精度。以结构的剩余寿命预测结果和维修资源为约束,以机队维修费用和保有率为目标,建立了一个基于视情维修的机队多目标维修决策优化模型。仿真结果表明,所提方法具有较好的预测精度,维修决策优化模型在保证结构安全的前提下,实现了维修成本和机队保有率的最优化。
The remaining useful life prediction of aircraft fatigue structure was greatly influenced by many uncertainty factors.To overcome this weakness,a new Remaining Useful Life(RUL)prediction method based on integrating the Extended Kalman Filter(EKF)algorithm with the real-time status data was proposed to alleviate the negative influence on prediction accuracy caused by the uncertainty factors.The prediction accuracy of RUL was significantly improved through updating the uncertain parameters of fatigue crack growth model in real time.Furthermore,on the basis of the obtained RUL information of structures,a CBM-based multi-objective decision making model concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet was established through taking into consideration of the maintenance resource.The numerical result demonstrated that the proposed method could estimate the RUL well and accurately identified the unknown parameters,and the established model was capable of obtaining optimization result which could simultaneously minimizing the maintenance cost and maximizing the availability on the premise of safety.
The remaining useful life prediction of aircraft fatigue structure was greatly influenced by many uncertainty factors.To overcome this weakness,a new Remaining Useful Life(RUL)prediction method based on integrating the Extended Kalman Filter(EKF)algorithm with the real-time status data was proposed to alleviate the negative influence on prediction accuracy caused by the uncertainty factors.The prediction accuracy of RUL was significantly improved through updating the uncertain parameters of fatigue crack growth model in real time.Furthermore,on the basis of the obtained RUL information of structures,a CBM-based multi-objective decision making model concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet was established through taking into consideration of the maintenance resource.The numerical result demonstrated that the proposed method could estimate the RUL well and accurately identified the unknown parameters,and the established model was capable of obtaining optimization result which could simultaneously minimizing the maintenance cost and maximizing the availability on the premise of safety.
引文
[1]HE Yuting,DU Xu,ZHANG Teng,et al.A few primary elements controlling aircraft structural service life[J].Journal of Air Force Engineering University:Natural Science Edition,2017,18(3):1-8(in Chinese).[何宇廷,杜旭,张腾,崔荣洪.飞机结构寿命控制中的几个基本问题[J].空军工程大学学报:自然科学版,2017,18(3):1-8.]
[2]WANG Y W,GOGU C,BINAUD N,et al.A cost driven predictive maintenance policy for structural airframe maintenance[J].Chinese Journal of Aeronautics,2017,30(3):1242-1257.
[3]BAI Shengbao,XIAO Yingchun,LIU Mabao,et al.Engineering applicability of monitoring crack by smart coatings sensor[J].Nondestructive Testing,2015,37(1):42-44(in Chinese).[白生宝,肖迎春,刘马宝,等.智能涂层传感器监测裂纹的工程适用性[J].无损检测,2015,37(1):42-44.]
[4]GAN Jie,ZENG Jianchao,ZHANG Xiaohong.Maintenance decision model with performance reliability constraint[J].Computer Integrated Manufacturing Systems,2016,22(4):1079-1087(in Chinese).[甘婕,曾建潮,张晓红.考虑性能可靠度约束的维修决策模型[J].计算机集成制造系统,2016,22(4):1079-1087.]
[5]LIN L,LUO B,ZHONG S S.Development and application of maintenance decision-making support system for aircraft fleet[J].Advances in Engineering Software,2017,114:192-207.
[6]YUAN Shenfang,ZHANG Hua,QIU Lei,et al.A fatigue crack growth prediction method based on particle filter[J].Acta Aeronautica et Astronautica Sinica,2013,34(12):2740-2747(in Chinese).[袁慎芳,张华,邱雷,等.基于粒子滤波算法的疲劳裂纹扩展预测方法[J].航空学报,2013,34(12):2740-2747.]
[7]WANG Xuliang,NIE Hong.Prediction method for fatigue life based on grey model GM(1,1)[J].Journal of Nanjing University of Aeronautics&Astronautics,2008,40(6):845-848(in Chinese).[王旭亮,聂宏.基于灰色系统GM(1,1)模型的疲劳寿命预测方法[J].南京航空航天大学学报,2008,40(6):845-848.]
[8]PAPADIMITRIOU C,FRITZEN C P,KRAEMER P,et al.Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering[J].Structural Control&Health Monitoring,2011,18(5):554-573.
[9]FENG Q,BI X,ZHAO X J,et al.Heuristic hybrid game approach for fleet condition-based maintenance planning[J].Reliability Engineering&System Safety,2017,157:166-176.
[10]BAI Fang.Methods of scheduling and condition based mainteance decision making in civil aero engine fleet[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2009:71-93(in Chinese).[白芳.民航发动机机群调度优化与视情维修决策方法研究[D].南京:南京航空航天大学,2009:71-93.]
[11]SAFAEI N,BANJEVIC D,JARDINE A K S.Workforceconstrained maintenance scheduling for military aircraft fleet:a case study[J].Annals of Operations Research,2011,186(1):295-316.
[12]KOZANIDIS G.A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements[J].Journal of Advanced Transportation,2009,43(2):155-182.
[13]VERMA A K,RAMESH P G.Multi-objective initial preventive maintenance scheduling for large engineering plants[J].International Journal of Reliability Quality&Safety Engineering,2007,14(3):241-250.
[14]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[15]CHOWDHARY G,JATEGAONKAR R.Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter[J].Aerospace Science&Technology,2010,14(2):106-117.
[16]XIA Tangbin.Research on dynamic process and predictive maintenance scheduling for health management of manufacturing systems[D].Shanghai:Shanghai Jiao Tong University,2014,10-12.[夏唐斌.面向制造系统健康管理的动态预测与预知维护决策研究[D].上海:上海交通大学,2014:10-12.]
[17]LIU J,WANG W,MA F,et al.A data-model-fusion prognostic framework for dynamic system state forecasting[J].Engineering Applications of Artificial Intelligence,2012,25(4):814-823.
[18]MOLENT L,BARTER S A.A comparison of crack growth behaviour in several full-scale airframe fatigue tests[J].International Journal of Fatigue,2007,29(6):1090-1099.
[19]NAKAGAWA T.Sequential imperfect preventive maintenance policies[J].IEEE Transactions on Reliability,1988,37(3):295-298.
[20]WU W F,NI C C.Statistical aspects of some fatigue crack growth data[J].Engineering Fracture Mechanics,2007,74(18):2952-2963.
[2]WANG Y W,GOGU C,BINAUD N,et al.A cost driven predictive maintenance policy for structural airframe maintenance[J].Chinese Journal of Aeronautics,2017,30(3):1242-1257.
[3]BAI Shengbao,XIAO Yingchun,LIU Mabao,et al.Engineering applicability of monitoring crack by smart coatings sensor[J].Nondestructive Testing,2015,37(1):42-44(in Chinese).[白生宝,肖迎春,刘马宝,等.智能涂层传感器监测裂纹的工程适用性[J].无损检测,2015,37(1):42-44.]
[4]GAN Jie,ZENG Jianchao,ZHANG Xiaohong.Maintenance decision model with performance reliability constraint[J].Computer Integrated Manufacturing Systems,2016,22(4):1079-1087(in Chinese).[甘婕,曾建潮,张晓红.考虑性能可靠度约束的维修决策模型[J].计算机集成制造系统,2016,22(4):1079-1087.]
[5]LIN L,LUO B,ZHONG S S.Development and application of maintenance decision-making support system for aircraft fleet[J].Advances in Engineering Software,2017,114:192-207.
[6]YUAN Shenfang,ZHANG Hua,QIU Lei,et al.A fatigue crack growth prediction method based on particle filter[J].Acta Aeronautica et Astronautica Sinica,2013,34(12):2740-2747(in Chinese).[袁慎芳,张华,邱雷,等.基于粒子滤波算法的疲劳裂纹扩展预测方法[J].航空学报,2013,34(12):2740-2747.]
[7]WANG Xuliang,NIE Hong.Prediction method for fatigue life based on grey model GM(1,1)[J].Journal of Nanjing University of Aeronautics&Astronautics,2008,40(6):845-848(in Chinese).[王旭亮,聂宏.基于灰色系统GM(1,1)模型的疲劳寿命预测方法[J].南京航空航天大学学报,2008,40(6):845-848.]
[8]PAPADIMITRIOU C,FRITZEN C P,KRAEMER P,et al.Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering[J].Structural Control&Health Monitoring,2011,18(5):554-573.
[9]FENG Q,BI X,ZHAO X J,et al.Heuristic hybrid game approach for fleet condition-based maintenance planning[J].Reliability Engineering&System Safety,2017,157:166-176.
[10]BAI Fang.Methods of scheduling and condition based mainteance decision making in civil aero engine fleet[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2009:71-93(in Chinese).[白芳.民航发动机机群调度优化与视情维修决策方法研究[D].南京:南京航空航天大学,2009:71-93.]
[11]SAFAEI N,BANJEVIC D,JARDINE A K S.Workforceconstrained maintenance scheduling for military aircraft fleet:a case study[J].Annals of Operations Research,2011,186(1):295-316.
[12]KOZANIDIS G.A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements[J].Journal of Advanced Transportation,2009,43(2):155-182.
[13]VERMA A K,RAMESH P G.Multi-objective initial preventive maintenance scheduling for large engineering plants[J].International Journal of Reliability Quality&Safety Engineering,2007,14(3):241-250.
[14]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[15]CHOWDHARY G,JATEGAONKAR R.Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter[J].Aerospace Science&Technology,2010,14(2):106-117.
[16]XIA Tangbin.Research on dynamic process and predictive maintenance scheduling for health management of manufacturing systems[D].Shanghai:Shanghai Jiao Tong University,2014,10-12.[夏唐斌.面向制造系统健康管理的动态预测与预知维护决策研究[D].上海:上海交通大学,2014:10-12.]
[17]LIU J,WANG W,MA F,et al.A data-model-fusion prognostic framework for dynamic system state forecasting[J].Engineering Applications of Artificial Intelligence,2012,25(4):814-823.
[18]MOLENT L,BARTER S A.A comparison of crack growth behaviour in several full-scale airframe fatigue tests[J].International Journal of Fatigue,2007,29(6):1090-1099.
[19]NAKAGAWA T.Sequential imperfect preventive maintenance policies[J].IEEE Transactions on Reliability,1988,37(3):295-298.
[20]WU W F,NI C C.Statistical aspects of some fatigue crack growth data[J].Engineering Fracture Mechanics,2007,74(18):2952-2963.