基于数字孪生的光电探测系统性能预测
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摘要
为解决光电探测系统由于多场耦合引起的性能退化建模难题,提出以光电探测系统的能量域表述为基础,建立数字孪生模型以解决其性能退化预测问题的方法。在该数字孪生模型的建立过程中,采用调制传递函数构建光电探测系统的静态性能物理模型;进一步采用动态贝叶斯网络表示调制传递函数随时间的演变规律,实现对其动态性能退化过程的图模型描述;最后通过粒子滤波算法实现系统的状态监测和性能预测,从而完成光电探测系统数字孪生模型的建立,并给出了具有不确定性估计的仿真验证结果,验证了数字孪生模型能够较好的解决光电探测系统的性能预测问题。
To solve the problem of electro-optical system for its performance monitoring,a digital twin model based on Dynamic Bayesian Network(DBN)was proposed.In this model,a system-level performance indicator from the perspective of energy domain using Modulation Transfer Function(MTF)was developed,which avoided tedious modelling of performance interactions between the multiple subsystems of electro-optical system.DBN was constructed from the evolution of MTF to denote the dynamic performance degradation process and the propagation of epistemic uncertainty.To make the digital twin model capable of tracking and predicting the system performance states,Particle Filter(PF)was proposed as the inference algorithm for DBN.A real dataset collected in the laboratory environment was used to validate the feasibility of the digital twin model and verify the effectiveness of PF inference algorithm.The results showed that the proposed method was effective for joint estimation of states and parameters,and the prediction of electro-optical system on-line health-status was achieved.
To solve the problem of electro-optical system for its performance monitoring,a digital twin model based on Dynamic Bayesian Network(DBN)was proposed.In this model,a system-level performance indicator from the perspective of energy domain using Modulation Transfer Function(MTF)was developed,which avoided tedious modelling of performance interactions between the multiple subsystems of electro-optical system.DBN was constructed from the evolution of MTF to denote the dynamic performance degradation process and the propagation of epistemic uncertainty.To make the digital twin model capable of tracking and predicting the system performance states,Particle Filter(PF)was proposed as the inference algorithm for DBN.A real dataset collected in the laboratory environment was used to validate the feasibility of the digital twin model and verify the effectiveness of PF inference algorithm.The results showed that the proposed method was effective for joint estimation of states and parameters,and the prediction of electro-optical system on-line health-status was achieved.
引文
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[2]CHEN X Z,YU J S,TANG D Y,et al.A novel PF-LSSVR-based framework for failure prognosis of nonlinear systems with time-varying parameters[J].Chinese Journal of Aeronautics,2012,25(5):715-724.
[3]RATCHES J A,LAWSON W R,OBERT L P,et al.Night vision laboratory static performance model for thermal viewing systems[EB/OL].[2018-03-25].http://adsabs.harvard.edu/abs/1975nvls.rept.R.
[4]GLAESSGEN,EDWARD H,STARGEL D S.The digital twin paradigm for future NASA and U.S.air force vehicles[C]//Proceedings of the 53rd Structures Dynamics and Materials Conference.Reston,Va.,USA:AIAA,2012:1-14.
[5]TUEGEL E,INGRAFFEA A,EASON T,et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011.DOI:10.1155/2011/154798.
[6]General Electric Company.GE digital twin:analytic engine for the digital power plant[R].Schenctady,N.Y.,USA:GE Power,2016.
[7]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Integrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[陶飞,张萌,程江峰,等.数字孪生车间:一种未来车间运行新模式[J].计算机集成制造系统,2017,23(1):1-9.]
[8]TAO Fei,LIU Weiran,LIU Jianhua,et al.Digital twin and its potential application exploration.2018,24(1):1-18(in Chinese).[陶飞,刘蔚然,刘检华,等.数字孪生及其应用探索[J].计算机集成制造系统,2018,24(1):1-18.]
[9]YU Yong,FAN Shengting,PENG Guanwei,et al.Study on application of digital twin model in product configuration management[J].Aeronautical Manufacturing Technology,2017,526(7):41-45(in Chinese).[于勇,范胜廷,彭关伟,等.数字孪生模型在产品构型管理中应用探讨[J].航空制造技术,2017,526(7):41-45.]
[10]WANG Zhensheng,MA Sasa,SONG Wei.Research on performance test system in outfield of thermal infrared imager[J].Ordnance Industry Automation,2011,30(11):63-67(in Chinese).[王真胜,马飒飒,宋伟.红外热像仪外场性能测试系统[J].兵工自动化,2011,30(11):63-67.]
[11]DRIGGERS R G,COX P G.National imagery interpretation rating system(NIIRS)and the probabilities of detection,recognition,and identification[J].Proceedings of SPIE-The International Society for Optical Engineering,1997,31(3/4):349-360.
[12]WIGREN C.Generic IRST detection performance model[J].Proceedings of SPIE-The International Society for Optical Engineering,2000,4030:206-217.
[13]ZHANG Peng.The research of infrared thermal imaging system distance[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2011(in Chinese).[张鹏.红外热成像系统作用距离的研究[D].南京:南京航空航天大学,2011.]
[14]SHI Ningning.Testing&evaluation of infrared imaging systems[D].Changchun:Changchun University of Science and Technology,2008(in Chinese).[石宁宁.红外成像系统的测试与评价[D].长春:长春理工大学,2008.]
[15]ZHANG Faqiang,ZHANG Yufa,DENG Qiang,et al.Model construction and simulation of electro-optical imaging system based on MTF[J].Laser&infrared,2015(5):549-554(in Chinese).[张发强,张玉发,邓强,等.基于MTF的光电成像系统建模仿真[J].激光与红外,2015(5):549-554.]
[16]XIE H,SHI J,LU W,et al.Dynamic Bayesian networks in electronic equipment health diagnosis[C]//Proceedings of Prognostics and System Health Management Conference(PHM-Chengdu).Washington,D.C.,USA:IEEE,2016:1-6.
[17]QIN A,ZHANG Q,HU Q,et al.Remaining useful life prediction for rotating machinery based on optimal degradation indicator[J].Shock and Vibration,2017(7):1-12.
[18]MURPHY K P.Dynamicbayesian networks:representation,inference and learning[M].Berkeley,Cal.,USA:University of California,2002.
[19]JOUIN M,GOURIVEAU R,HISSEL D,et al.Particle filter-based prognostics:Review,discussion and perspectives[J].Mechanical Systems&Signal Processing,2016:72/73:2-31.
[20]HU Y,BARALDI P,MAIO F D,et al.A particle filtering and kernel smoothing-based approach for new design component prognostics[J].Reliability Engineering&System Safety,2015,134:19-31.