双组元推进系统的部分可观时间Petri网故障诊断
|
摘要
研究了基于部分可观时间Petri网双组元推进系统的故障诊断问题。针对双组元推进系统中环境复杂且部分关键信息无法通过传感器获取的情况,结合部分可观时间Petri网,提出构建修正状态类图的部分可观时间Petri网故障诊断方法。系统过程的节点对应为可观测变迁和不可观测变迁,结合变迁同步、异步触发关系,标定各变迁时间区间,建立部分可观时间Petri网模型,然后转化为修正状态类图。遍历所有满足可观测变迁触发时间和序列信息的路径,诊断系统是否发生故障。最后对双组元推进系统建立部分可观时间Petri网模型,结合系统工作过程中各执行机构可观测状态,对系统不可观部分进行故障诊断,验证了算法的有效性。
The fault diagnosis of the bipropellant propulsion system is investigated with partially observed time Petri nets TPN.The internal environment of the bipropellant propulsion system is complex and some key information can not be obtained through the sensors.A method of building modified state class graphs based on partially observed(TPN)is proposed.The node of the system process corresponds to observable transition and unobservable transition.The time interval of each transition is calibrated,and partially observed TPN is built.And then they are transformed into modified state class graphs based on the different relationships of the synchronization and asynchronous transitions.Traverse all paths that satisfy the fire time of observable transition and fire sequences to determine whether the system is faulty.Finally,the partially observed TPN model of the bipropellant propulsion system is built.Based on the observability of each actuator in the system process,fault diagnosis of unobservable part in the system is performed to verify the effectiveness of the algorithm.
The fault diagnosis of the bipropellant propulsion system is investigated with partially observed time Petri nets TPN.The internal environment of the bipropellant propulsion system is complex and some key information can not be obtained through the sensors.A method of building modified state class graphs based on partially observed(TPN)is proposed.The node of the system process corresponds to observable transition and unobservable transition.The time interval of each transition is calibrated,and partially observed TPN is built.And then they are transformed into modified state class graphs based on the different relationships of the synchronization and asynchronous transitions.Traverse all paths that satisfy the fire time of observable transition and fire sequences to determine whether the system is faulty.Finally,the partially observed TPN model of the bipropellant propulsion system is built.Based on the observability of each actuator in the system process,fault diagnosis of unobservable part in the system is performed to verify the effectiveness of the algorithm.
引文
[1]YANG H,JIANG B,COCQUEMPOT V,et al.Spacecraft formation stabilization and fault tolerance:a state-varying switched system approach[J].Systems&Control Letters,2013,62(9):715-722.
[2]ZHANG X,TANG L,DECASTRO J.Robust fault diagnosis of aircraft engines:a nonlinear adaptive estimation-based approach[J].IEEE Trans.on Control Systems Technology,2013,21(3):861-868.
[3]姜连祥,李华旺,杨根庆,等.航天器自主故障诊断技术研究进展[J].宇航学报,2009,30(4):28-34.JIANG L X,LI H W,YANG G Q.A survey of spacecraft autonomous fault diagnosis research[J].Journal of Astronautics,2009,30(4):28-34.
[4]SEMMEL G S,DAVIS S R,LEUCHT K W,et al.Space shuttle ground processing with monitoring agents[J].IEEE Intelligent Systems,2006,21(1):68-73.
[5]NACI Z,GREG R L.Stability of gas pressure regulators[J].Applied Mathematical Modeling,2008,32(1):61-82.
[6]宋其江,徐敏强,王日新.基于分层有向图的航天器故障诊断[J].航空学报,2009,30(6):1058-1062.SONG Q J,XU M Q,WANG R X.Spacecraft fault diagnosis based on hierarchical digraphs[J].Acta Aeronautica et Astronautica Sinica,2009,30(6):1058-1062.
[7]HWANG I,KIM S,KIM Y,et al.A survey of fault detection,isolation,and reconfiguration methods[J].IEEE Trans.on Control Systems Technology,2010,18(3):636-653.
[8]CABASINO M P,GIUA A,SEATZU C.Diagnosability of discrete event systems using labeled Petri nets[J].IEEE Trans.on Automation Science and Engineering,2014,11(1):144-153.
[9]DECLERCK P,BONHOMMME P.State estimation of timed labeled Petri nets with unobservable transitions[J].IEEE Trans.on Automation Science and Engineering,2014,11(1):103-110.
[10]BASILE F,CORDONE R,PIRODDI L.A branch and bound approach for the design of decentralized supervisors in Petri net models[J].Automatica,2015,52(C):322-333.
[11]SHEN Q,QIU J,LIU G,et al.Intermittent fault’s parameter framework and stochastic petri net based formalization model[J].Eksploatacja I Niezawodnosc-Maintenance and Reliability,2016,18(2):210-217.
[12]WANG L,CHEN Q,GAO Z,et al.Knowledge representation and general Petri net models for power grid fault diagnosis[J].IET Generation,Transmission&Distribution,2015,9(9):866-873.
[13]CABRAL F G,MOREIRA M V,DIENE O,et al.A Petri net diagnoser for discrete event systems modeled by finite state automata[J].IEEE Trans.on Automatic Control,2015,60(1):59-71.
[14]BONHOMME P.Marking estimation of P-time Petri nets with unobservable transitions[J].IEEE Trans.on Systems,Man,and Cybernetics:Systems,2015,45(3):508-518.
[15]SIMONA B,JAVIER C,JOSE M.Timing-failure risk assessment of UML design using time Petri net bound techniques[J].IEEE Trans.on Industrial Informatics,2011,7(1):90-104.
[16]GIUA A,SEATZU C.Observability of place/transition nets[J].IEEE Trans.on Automatic Control,2002,47(9):1424-1437.
[17]CABASINO M P,GIUA A,SEATZU C.Fault detection for discrete event system using Petri nets with unobserved transitions[J].Automatica,2010,46(9):1531-1539.
[18]CORDONE R,PIRODDI L.Parsimonious monitor control of Petri net models of flexible manufacturing systems[J].IEEE Trans.on System,Man,and Cybernetics:Systems,2013,43(1):215-221.
[19]CHEN Y F,LI Z W,Barkaoui K,et al.New Petri nets structure and its application to optimal supervisory control:interval inhibitor arcs[J].IEEE Trans.on Systems,Man,and Cybernetics:Systems,2014,44(10),1384-1400.
[20]CABASINO M P,GIUA A,SEATZU C,et al.Fault diagnosis of an ABS system using Petri nets[C]∥Proc.of the IEEE International Conference on Automation Science and Engineering,2011:24-27.
[21]LEFEBVRE D.On-line fault diagnosis with partially observed Petri nets[J].IEEE Trans.on Automatic Control,2014,59(7):1919-1924.
[22]BASILE F,CHIACCHIO P,TOMMASI D G.On diagnosability of Petri nets via integer linear programming[J].Automatica,2012,48(9):2047-2058.
[23]DARBY R.The dynamic response of pressure relief valves in vapor or gas service[J].Journal of Loss Prevention in the Process Industries,2013,26(6):1262-1268.
[24]LI L,HADJICOSTIS C N.Minimun initial marking estimation in labeled Petri nets[J].IEEE Trans.on Automatic Control,2013,58(1):198-203.
[25]王亮,吕卫民,滕克难,等.基于Petri网的复杂设备健康状态退化分析[J].系统工程与电子技术,2014,36(10):1973-1981.WANG L,LUW M,TENG K N,et al.Health degradation analysis of complex equipment based on Petri nets[J].Systems Engineering and Electronics,2014,36(10):1973-1981.
[26]KO S.Performance comparison of covariance-assignment state estimators with intermittent observations[J].International Journal of Control Automation and Systems,2015,13(6):1391-1401.
[27]CABASINO M P,LAFORTUNE S,SEATZU C.Optimal sensor selection for ensuring diagnosability in labeled petri nets[J].Automatica,2013,49(8):2373-2383.
[28]LIME D,ROUX O H.Model checking of time Petri nets using the state class timed automaton[J].Discrete Event Dynamic Systems:Theory and Application,2006,16(2):179-205.
[29]鲁峰,黄金泉,吕怡秋,等.基于非线性自适应滤波的发动机气路部件健康诊断方法[J].航空学报,2013,34(11):2529-2538.LU F,HUANG J Q,LUY Q,et al.Aircraft engine gas-path components health diagnosis based on nonlinear adaptive filters[J].Acta Aeronautica et Astronautica Sinica,2013,34(11):2529-2538.
[30]陈金豹,翟国富,王淑娟,等.航天电子设备多余物检测信号特征的影响因素分析[J].系统工程与电子技术,2013,35(4):889-894.CHEN J B,ZHAI G F,WANG S J,et al.Factors affecting characteristics of acoustic signals in particle impact noise detection for aerospace devices[J].Systems Engineering and Electronics,2013,35(4):889-894.
[31]WANG X,CRISTIAN M,MANUEL S.Diagnosis of time Petri nets using fault diagnosis graph[J].IEEE Trans.on Automatic Control,2015,60(9):2321-2335.
[2]ZHANG X,TANG L,DECASTRO J.Robust fault diagnosis of aircraft engines:a nonlinear adaptive estimation-based approach[J].IEEE Trans.on Control Systems Technology,2013,21(3):861-868.
[3]姜连祥,李华旺,杨根庆,等.航天器自主故障诊断技术研究进展[J].宇航学报,2009,30(4):28-34.JIANG L X,LI H W,YANG G Q.A survey of spacecraft autonomous fault diagnosis research[J].Journal of Astronautics,2009,30(4):28-34.
[4]SEMMEL G S,DAVIS S R,LEUCHT K W,et al.Space shuttle ground processing with monitoring agents[J].IEEE Intelligent Systems,2006,21(1):68-73.
[5]NACI Z,GREG R L.Stability of gas pressure regulators[J].Applied Mathematical Modeling,2008,32(1):61-82.
[6]宋其江,徐敏强,王日新.基于分层有向图的航天器故障诊断[J].航空学报,2009,30(6):1058-1062.SONG Q J,XU M Q,WANG R X.Spacecraft fault diagnosis based on hierarchical digraphs[J].Acta Aeronautica et Astronautica Sinica,2009,30(6):1058-1062.
[7]HWANG I,KIM S,KIM Y,et al.A survey of fault detection,isolation,and reconfiguration methods[J].IEEE Trans.on Control Systems Technology,2010,18(3):636-653.
[8]CABASINO M P,GIUA A,SEATZU C.Diagnosability of discrete event systems using labeled Petri nets[J].IEEE Trans.on Automation Science and Engineering,2014,11(1):144-153.
[9]DECLERCK P,BONHOMMME P.State estimation of timed labeled Petri nets with unobservable transitions[J].IEEE Trans.on Automation Science and Engineering,2014,11(1):103-110.
[10]BASILE F,CORDONE R,PIRODDI L.A branch and bound approach for the design of decentralized supervisors in Petri net models[J].Automatica,2015,52(C):322-333.
[11]SHEN Q,QIU J,LIU G,et al.Intermittent fault’s parameter framework and stochastic petri net based formalization model[J].Eksploatacja I Niezawodnosc-Maintenance and Reliability,2016,18(2):210-217.
[12]WANG L,CHEN Q,GAO Z,et al.Knowledge representation and general Petri net models for power grid fault diagnosis[J].IET Generation,Transmission&Distribution,2015,9(9):866-873.
[13]CABRAL F G,MOREIRA M V,DIENE O,et al.A Petri net diagnoser for discrete event systems modeled by finite state automata[J].IEEE Trans.on Automatic Control,2015,60(1):59-71.
[14]BONHOMME P.Marking estimation of P-time Petri nets with unobservable transitions[J].IEEE Trans.on Systems,Man,and Cybernetics:Systems,2015,45(3):508-518.
[15]SIMONA B,JAVIER C,JOSE M.Timing-failure risk assessment of UML design using time Petri net bound techniques[J].IEEE Trans.on Industrial Informatics,2011,7(1):90-104.
[16]GIUA A,SEATZU C.Observability of place/transition nets[J].IEEE Trans.on Automatic Control,2002,47(9):1424-1437.
[17]CABASINO M P,GIUA A,SEATZU C.Fault detection for discrete event system using Petri nets with unobserved transitions[J].Automatica,2010,46(9):1531-1539.
[18]CORDONE R,PIRODDI L.Parsimonious monitor control of Petri net models of flexible manufacturing systems[J].IEEE Trans.on System,Man,and Cybernetics:Systems,2013,43(1):215-221.
[19]CHEN Y F,LI Z W,Barkaoui K,et al.New Petri nets structure and its application to optimal supervisory control:interval inhibitor arcs[J].IEEE Trans.on Systems,Man,and Cybernetics:Systems,2014,44(10),1384-1400.
[20]CABASINO M P,GIUA A,SEATZU C,et al.Fault diagnosis of an ABS system using Petri nets[C]∥Proc.of the IEEE International Conference on Automation Science and Engineering,2011:24-27.
[21]LEFEBVRE D.On-line fault diagnosis with partially observed Petri nets[J].IEEE Trans.on Automatic Control,2014,59(7):1919-1924.
[22]BASILE F,CHIACCHIO P,TOMMASI D G.On diagnosability of Petri nets via integer linear programming[J].Automatica,2012,48(9):2047-2058.
[23]DARBY R.The dynamic response of pressure relief valves in vapor or gas service[J].Journal of Loss Prevention in the Process Industries,2013,26(6):1262-1268.
[24]LI L,HADJICOSTIS C N.Minimun initial marking estimation in labeled Petri nets[J].IEEE Trans.on Automatic Control,2013,58(1):198-203.
[25]王亮,吕卫民,滕克难,等.基于Petri网的复杂设备健康状态退化分析[J].系统工程与电子技术,2014,36(10):1973-1981.WANG L,LUW M,TENG K N,et al.Health degradation analysis of complex equipment based on Petri nets[J].Systems Engineering and Electronics,2014,36(10):1973-1981.
[26]KO S.Performance comparison of covariance-assignment state estimators with intermittent observations[J].International Journal of Control Automation and Systems,2015,13(6):1391-1401.
[27]CABASINO M P,LAFORTUNE S,SEATZU C.Optimal sensor selection for ensuring diagnosability in labeled petri nets[J].Automatica,2013,49(8):2373-2383.
[28]LIME D,ROUX O H.Model checking of time Petri nets using the state class timed automaton[J].Discrete Event Dynamic Systems:Theory and Application,2006,16(2):179-205.
[29]鲁峰,黄金泉,吕怡秋,等.基于非线性自适应滤波的发动机气路部件健康诊断方法[J].航空学报,2013,34(11):2529-2538.LU F,HUANG J Q,LUY Q,et al.Aircraft engine gas-path components health diagnosis based on nonlinear adaptive filters[J].Acta Aeronautica et Astronautica Sinica,2013,34(11):2529-2538.
[30]陈金豹,翟国富,王淑娟,等.航天电子设备多余物检测信号特征的影响因素分析[J].系统工程与电子技术,2013,35(4):889-894.CHEN J B,ZHAI G F,WANG S J,et al.Factors affecting characteristics of acoustic signals in particle impact noise detection for aerospace devices[J].Systems Engineering and Electronics,2013,35(4):889-894.
[31]WANG X,CRISTIAN M,MANUEL S.Diagnosis of time Petri nets using fault diagnosis graph[J].IEEE Trans.on Automatic Control,2015,60(9):2321-2335.