间歇式反应釜的故障诊断及容错控制设计
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摘要
随着现代工业控制系统复杂程度的增加和人们对系统性能指标要求的提高,系统的可靠性,可维护性和容错性受到了人们越来越多的重视。由此,故障检测及容错控制方法在近二十年一直作为控制界的研究热点,取得了显著的成果。
本文从研究间歇式反应釜釜内温度控制出发,对于采用串级控制方式控制反应釜的釜内温度在主传感器失效的情况下,对其进行了深入的故障诊断及容错控制的研究,主要进行了以下几方面的研究工作。
1)进行了利用人工神经网络建立反应釜控制回路的故障诊断系统的研究。为了保证网络训练数据的完备性,本文从求取对象系统故障树最小割集入手,通过系统故障模式获取训练样本。另外,考虑到复杂大型神经网络教师数据整理困难,学习精度和学习效率低下等问题。本文采用了复合模糊神经网络技术建立了反应釜的故障诊断系统。
2)提出了一种基于辅助模型的最小二乘算法,对反应釜进行数学建模研究.论文根据对象的特点确定了模型结构,利用辅助模型来估算冷却水温度、冷却水流量和夹套温度对釜温的影响,最后利用最小二乘法进行参数辨识完成了对象系统的模型建立。该方法克服了标准最小二乘法由于无法辨识模型中未知中间变量而无法进行建模的困难,经Matlab对系统模型进行仿真,模型输出基本与实际输出符合,为对系统进行进一步的容错控制垫定了基础。
3)利用所得的数学模型采用信号重构的方式设计了对象反应釜在控制系统主传感器失效时控制重构的容错控制方案。在故障检测单元激励下,利用数学模型估算的输出值取代主传感器的输出反馈回控制器,保证系统仍然可以正常运行。并利Matlab对设计的容错控制系统进行了仿真试验研究,仿真实验结果表明了,所设计的容错控制系统可以保证系统的稳定运行。
With complexity of modern control systems increasing and the requirement of higher performance of the system, reliability, maintainability and fault-tolerant capacity have attracted more and more attention of the world. For two decade, fault detection and tolerant control have made great improvement, and many remarkable results have been obtained.
Starting with temperature control of the Vessel proceeding Batch Reactor, this paper makes a further studying on fault detection and tolerant control in condition of using cascade control to the reactor temperature as the master sensor fails to work, the main research work on it is done as follows:
1) In this paper, for the use of artificial neural network to build the reactor control circuit fault diagnosis system. In order to ensure a complete network of training data, this article start from striking a system fault tree minimal cut , through the system failure mode to obtain training samples. In addition, taking into account the complicated and large-scale neural network will be faced with hard data collated teachers, low learning precision and low learning efficiency. In this paper, the composite fuzzy neural network technology was used to build the reactor's fault diagnosis system.
2) An identification algorithm based on auxiliary models and least squares is developed and used to model the Batch Reactor. According to the characteristic of the reactor, the paper determines the model structure, and using auxiliary models to estimate Jacket temperature, cooling water temperature and the cooling water flow impact on the reactor temperature, and then, using least squares principle to produce the parameters. This method overcome the difficulties that standard least square does not work when the inner variables are unmeasurable. The simulated result shows that the algorithms proposed are effective and provides the basis for fault tolerant control.
3) Using signal reconstruction to achieve a cascade control as sensor fails to work in the main control with proceeds mathematical mode. In the stimulation from fault detection unit, the feedback controller, with the inputs from the mathematical model estimates instead of the inputs from the master sensor, can make the systems in the normal operation. Finally, the simulation results show that, when the measurement of certain components in the system fails to work, leading to the lost of control signal, the assumptions to realize the system Fault Tolerant Control through the use of results calculated by the system model is totally feasible.
本文从研究间歇式反应釜釜内温度控制出发,对于采用串级控制方式控制反应釜的釜内温度在主传感器失效的情况下,对其进行了深入的故障诊断及容错控制的研究,主要进行了以下几方面的研究工作。
1)进行了利用人工神经网络建立反应釜控制回路的故障诊断系统的研究。为了保证网络训练数据的完备性,本文从求取对象系统故障树最小割集入手,通过系统故障模式获取训练样本。另外,考虑到复杂大型神经网络教师数据整理困难,学习精度和学习效率低下等问题。本文采用了复合模糊神经网络技术建立了反应釜的故障诊断系统。
2)提出了一种基于辅助模型的最小二乘算法,对反应釜进行数学建模研究.论文根据对象的特点确定了模型结构,利用辅助模型来估算冷却水温度、冷却水流量和夹套温度对釜温的影响,最后利用最小二乘法进行参数辨识完成了对象系统的模型建立。该方法克服了标准最小二乘法由于无法辨识模型中未知中间变量而无法进行建模的困难,经Matlab对系统模型进行仿真,模型输出基本与实际输出符合,为对系统进行进一步的容错控制垫定了基础。
3)利用所得的数学模型采用信号重构的方式设计了对象反应釜在控制系统主传感器失效时控制重构的容错控制方案。在故障检测单元激励下,利用数学模型估算的输出值取代主传感器的输出反馈回控制器,保证系统仍然可以正常运行。并利Matlab对设计的容错控制系统进行了仿真试验研究,仿真实验结果表明了,所设计的容错控制系统可以保证系统的稳定运行。
With complexity of modern control systems increasing and the requirement of higher performance of the system, reliability, maintainability and fault-tolerant capacity have attracted more and more attention of the world. For two decade, fault detection and tolerant control have made great improvement, and many remarkable results have been obtained.
Starting with temperature control of the Vessel proceeding Batch Reactor, this paper makes a further studying on fault detection and tolerant control in condition of using cascade control to the reactor temperature as the master sensor fails to work, the main research work on it is done as follows:
1) In this paper, for the use of artificial neural network to build the reactor control circuit fault diagnosis system. In order to ensure a complete network of training data, this article start from striking a system fault tree minimal cut , through the system failure mode to obtain training samples. In addition, taking into account the complicated and large-scale neural network will be faced with hard data collated teachers, low learning precision and low learning efficiency. In this paper, the composite fuzzy neural network technology was used to build the reactor's fault diagnosis system.
2) An identification algorithm based on auxiliary models and least squares is developed and used to model the Batch Reactor. According to the characteristic of the reactor, the paper determines the model structure, and using auxiliary models to estimate Jacket temperature, cooling water temperature and the cooling water flow impact on the reactor temperature, and then, using least squares principle to produce the parameters. This method overcome the difficulties that standard least square does not work when the inner variables are unmeasurable. The simulated result shows that the algorithms proposed are effective and provides the basis for fault tolerant control.
3) Using signal reconstruction to achieve a cascade control as sensor fails to work in the main control with proceeds mathematical mode. In the stimulation from fault detection unit, the feedback controller, with the inputs from the mathematical model estimates instead of the inputs from the master sensor, can make the systems in the normal operation. Finally, the simulation results show that, when the measurement of certain components in the system fails to work, leading to the lost of control signal, the assumptions to realize the system Fault Tolerant Control through the use of results calculated by the system model is totally feasible.
引文
[1]Caglayan A K.Evaluation of a second generation reconfiguration strategy for aircraft flight control systems subjected to actuator failure surface damage.1988,proc.of the IEEE National Aerospace and Electronic Conference,520-529.
[2]Stengel F R.Toward intelligent flight control.IEEE Trans.Syst,Man,Cybem,1993,23(6).1699-1717.
[3]Wang Z,Huang B,Unbehauen H.Robust reliable control for a class of uncertain nonlinear state-delayed systems.Automatica,1999,35(6):955-963.
[4]Yang G H.Reliable control using redundant controller.IEEE Trans.on Autom.Control,1998,43(11):1588-1593.
[5]周东华,王贵增.故障诊断技术:综述.化工自动化与仪表,1998,25(1):58-62.
[6]张洪钺,闻新,周露.国内控制系统故障诊断技术的现状与展望.火力与指挥控制,1997,22(3):1-6.
[7]Beard R V.Failure accommodation in linear systems through self-reorganization.Report MVT-71-1,Man Vehicle Lab,MIT,Cambridge,Massachusetts,1971.
[8]A S.Willsky.A Survey of Design Methods for Failure Detection in Dynamic Systems.Automatica,1976,12:601-611.
[9]D.M.Himmelblau.Fault Detection and Diagnosis in Chemical and Petrochemical Process.Amsterdam,Elsevier Press.1978.
[10]R.Isermann.Process Fault-detection Based on Modeling and Estimation Methods:A Survey.Automatica,1984,20:387-404.
[11]叶银忠,潘日芳,蒋慰孙.动态系统的故障检测与诊断方法.信息与控制,1985,14(6):27-34.
[12]J.J.Gertler.Survey of Model Based Failure Detection and Isolation in Complex Plants.IEEE Control Systems Magazine,1988,8(6):3-11.
[13]R.J.Patton,et al.Fault Diagnosis in Dynamic Systems.New York:Prentice Hall,1989.
[14]J.Stoustrup,H.H.Niemann.Fault Estimation-A Standard Problem Approach.International Journal of Robust and Nonlinear Control,2002,12:649- 673.
[15]P.M.Frank.Fault Diagnosis in Dynamic Systems using Analytical and Knowledge-Based Redundancy--A Survey and Some New Results.Automatica,1990,26(3):459-474.
[16]R.Isermann.Fault Diagnosis of Machines via Parameter Estimation and Knowledge Processing:Tutorial Paper.Automatica,1993,29(4):814-835.
[17]M.Basseville.Detection of Abrupt Changes in Signal and Dynamic Systems.Berline:Springer-Verlag,1985.
[18]周东华,孙优贤.控制系统的故障检测与诊断技术.清华大学出版社,1994.
[19]张育林,李东旭.动态系统故障诊断理论与应用.长沙:国防科技大学出版社,1997.
[20]C.Commault,J.M.Dion,O.Sename,R.Motyeian.Observer Based Fault Detection for Structured Systems.IEEE Transaction on Automatic Control,2002,47(12):3073-2079.
[21]Puneet Goel,Goksel Dedeoglu,Stergios IRoumeliotis.Fault detection and identification in a mobile robot using multiple model estimation and neural network.In proc.2000 IEEE International Conference on Robotics and Automation,San Francisco,Californiak,2000.2302-2309.
[22]Qinghua Zhang.Adaptive observer for MIMO linear time marrying system.Rappert de Recherche IRISA No.1379.submitted to IEEE Trans.On automatic Control 2001.
[23]Alexander Medvedev.Disturbance attenuation enhancement in continuous parity space methods.European Control Conference ECC97,Sweden 1997.641-647.
[24]Kumamaru K.Robust fault detection using index of kullback discrimination information.In Proc.of IFAC World Congress.1996.205-210.
[25]Racz A,Pazsit I.Diagnostics of detector tube impacting with wavelet techniques annals of Nuclear energy,10-DEC-97,387-400.
[26]Shaoying Liu,John A McKermid.A model-oriented approach to safety analysis using fault trees and a support system.The journal of systems and Software,Elsevier Science,1996,(2):151-164.
[27]胡寿松,于源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5):617-620.
[28]Hayes-Roth.Building Expert Systems.Addison-Wesley,1983.
[29]Vemuri A T,Marios M P.Neural betwork-basedrobust fault diagnosis in robotic systems.IEEE Trans.Neural Networks,1997,8(6):1410-1419.
[30]Tomsovic K,Baer B.Fuzzy information approaches to equipment condition monitoring and diagnosis.Australian Journal of Intelligent Information Processing Systems,1998,3(1):23-31.
[31]Alexander Y,Ronald L.Recognition of bearing failures using wavelets and neural networks.TFTS97,Coventry(UK),August 27-29,1997.
[32]Mane Skarstein Bjanger.Vibration analysis in rotating machinery using rough set theory and ROSETTA.Report of Department of Computer and Information Science Norwegian University of Science and Technology,1999.
[33]Niederlinski A.A Heuristic Approach to the Design of Interacting Multivariable Systems.Automatica.1971,(7):691-701.
[34]Gao Z,Antsaklis P J.Stability of the pseudo-inverse method for reconfigurable control systems.Int.J Controt,1991,53(3):717-729.
[35]Jiang J.Design of reconfigurable control system using eigenstructure assignments.Int.J.Control,1994,59(2):395-410
[36]Huber R R,McCulloch B.Self-repairing flight control system.SAE Tech.Paper Series,1984,1-20.
[37]Soloway,Donald,Haley,et al.Reconfigurable flight control using neural generalized predictive control.AIAA Space 2000 Conference and Exposition,Long Beach CA,Sept.19-21,2000.
[38]胡寿松,周川.基于RBF神经网络的模型跟随非线性自修复控制.南京航空航天大学学报,1998,30(5):486-490.
[39]Hang-Ming Liaw,Yeong-May Lin,Kuei-Hsiang Chao.AVSS speed controller with model reference response for induction motor Drive.IEEE Trans.on Industrial Electronics,2001,48(6):1136-1142.
[40]Gao Z,Antsaklis P J.Reconfigurable control systems design via perfect model following.International Journal of Control,1992,56:783-798.
[41]邹益仁,马增良,蒲维编.现场总线控制系统的设计和开发。北京:国防工业出版社,2003.
[42]梅启智.系统可靠性工程基础。科学出版社,1992.
[43]苏晓勤,故障树分析算法改进研究与实现,2005.
[44]T.Song,Y.Shimada,K.Suzuki,H.Sayama Automated Fault Tree Synthesis Using Event Relation Matrix,Journal of the Socitey of Plant Engineers japan,1997,18(4):8-15
[45]申韬,韩守木,黄树红,刘德昌.汽轮发电机组故障诊断的复合式进化网络,中理工大学学报.1998.
[46]杨建刚.人工神经网络实用教程.杭州:浙江大学出版社,2002.
[47]李鹏波,胡德文.系统辨识基础.北京:中国水利水电出版社,2006.
[48]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真.北京:科学出版社,2004.
[49]潘立登,潘仰东.系统辨识与建模.北京:化学工业出版社,2004.
[50]邓自立,王欣,高媛等.建模与估计.北京:科学出版社,2007
[51]F Ding,T.Chen,Combined parameter and output estimation of dual-rate systems using an auxiliary model,Automatica 40(10)(2004)1739-1748
[52]Jie Ding,Feng Ding,Shi Zhang,Parameter identification of multi-input,single-output systems based on FIR models and least squares principle,Applied Mathematics and Computation,Volume 197,Issuel,15 March2008,pages197-305
[53]施吉林,张宏伟,金光日.计算机科学计算.北京:高等教育出版社,2005
[54]魏巍.控制工程工具箱技术手册.北京:国防工业出版社,2004
[55]黄永安,马路,刘慧敏.MATLAB7.0/Simulink6.0建模仿真开发与高级工程应用.北京:清华大学出版社,2005
[56]王政林,郭阳宽.过程控制与Simulink应用.北京:电子工业出版社,2006.
[57]赵明旺.容错控制系统动态稳定的控制律重构.控制理论与应用,1999,16(1):123-126.
[58]吴文海,危琦,于建立.用状态观测器重构飞行控制律研究.飞机设计,2000,(4):7-11.
[2]Stengel F R.Toward intelligent flight control.IEEE Trans.Syst,Man,Cybem,1993,23(6).1699-1717.
[3]Wang Z,Huang B,Unbehauen H.Robust reliable control for a class of uncertain nonlinear state-delayed systems.Automatica,1999,35(6):955-963.
[4]Yang G H.Reliable control using redundant controller.IEEE Trans.on Autom.Control,1998,43(11):1588-1593.
[5]周东华,王贵增.故障诊断技术:综述.化工自动化与仪表,1998,25(1):58-62.
[6]张洪钺,闻新,周露.国内控制系统故障诊断技术的现状与展望.火力与指挥控制,1997,22(3):1-6.
[7]Beard R V.Failure accommodation in linear systems through self-reorganization.Report MVT-71-1,Man Vehicle Lab,MIT,Cambridge,Massachusetts,1971.
[8]A S.Willsky.A Survey of Design Methods for Failure Detection in Dynamic Systems.Automatica,1976,12:601-611.
[9]D.M.Himmelblau.Fault Detection and Diagnosis in Chemical and Petrochemical Process.Amsterdam,Elsevier Press.1978.
[10]R.Isermann.Process Fault-detection Based on Modeling and Estimation Methods:A Survey.Automatica,1984,20:387-404.
[11]叶银忠,潘日芳,蒋慰孙.动态系统的故障检测与诊断方法.信息与控制,1985,14(6):27-34.
[12]J.J.Gertler.Survey of Model Based Failure Detection and Isolation in Complex Plants.IEEE Control Systems Magazine,1988,8(6):3-11.
[13]R.J.Patton,et al.Fault Diagnosis in Dynamic Systems.New York:Prentice Hall,1989.
[14]J.Stoustrup,H.H.Niemann.Fault Estimation-A Standard Problem Approach.International Journal of Robust and Nonlinear Control,2002,12:649- 673.
[15]P.M.Frank.Fault Diagnosis in Dynamic Systems using Analytical and Knowledge-Based Redundancy--A Survey and Some New Results.Automatica,1990,26(3):459-474.
[16]R.Isermann.Fault Diagnosis of Machines via Parameter Estimation and Knowledge Processing:Tutorial Paper.Automatica,1993,29(4):814-835.
[17]M.Basseville.Detection of Abrupt Changes in Signal and Dynamic Systems.Berline:Springer-Verlag,1985.
[18]周东华,孙优贤.控制系统的故障检测与诊断技术.清华大学出版社,1994.
[19]张育林,李东旭.动态系统故障诊断理论与应用.长沙:国防科技大学出版社,1997.
[20]C.Commault,J.M.Dion,O.Sename,R.Motyeian.Observer Based Fault Detection for Structured Systems.IEEE Transaction on Automatic Control,2002,47(12):3073-2079.
[21]Puneet Goel,Goksel Dedeoglu,Stergios IRoumeliotis.Fault detection and identification in a mobile robot using multiple model estimation and neural network.In proc.2000 IEEE International Conference on Robotics and Automation,San Francisco,Californiak,2000.2302-2309.
[22]Qinghua Zhang.Adaptive observer for MIMO linear time marrying system.Rappert de Recherche IRISA No.1379.submitted to IEEE Trans.On automatic Control 2001.
[23]Alexander Medvedev.Disturbance attenuation enhancement in continuous parity space methods.European Control Conference ECC97,Sweden 1997.641-647.
[24]Kumamaru K.Robust fault detection using index of kullback discrimination information.In Proc.of IFAC World Congress.1996.205-210.
[25]Racz A,Pazsit I.Diagnostics of detector tube impacting with wavelet techniques annals of Nuclear energy,10-DEC-97,387-400.
[26]Shaoying Liu,John A McKermid.A model-oriented approach to safety analysis using fault trees and a support system.The journal of systems and Software,Elsevier Science,1996,(2):151-164.
[27]胡寿松,于源.基于支持向量机的非线性系统故障诊断.控制与决策,2001,16(5):617-620.
[28]Hayes-Roth.Building Expert Systems.Addison-Wesley,1983.
[29]Vemuri A T,Marios M P.Neural betwork-basedrobust fault diagnosis in robotic systems.IEEE Trans.Neural Networks,1997,8(6):1410-1419.
[30]Tomsovic K,Baer B.Fuzzy information approaches to equipment condition monitoring and diagnosis.Australian Journal of Intelligent Information Processing Systems,1998,3(1):23-31.
[31]Alexander Y,Ronald L.Recognition of bearing failures using wavelets and neural networks.TFTS97,Coventry(UK),August 27-29,1997.
[32]Mane Skarstein Bjanger.Vibration analysis in rotating machinery using rough set theory and ROSETTA.Report of Department of Computer and Information Science Norwegian University of Science and Technology,1999.
[33]Niederlinski A.A Heuristic Approach to the Design of Interacting Multivariable Systems.Automatica.1971,(7):691-701.
[34]Gao Z,Antsaklis P J.Stability of the pseudo-inverse method for reconfigurable control systems.Int.J Controt,1991,53(3):717-729.
[35]Jiang J.Design of reconfigurable control system using eigenstructure assignments.Int.J.Control,1994,59(2):395-410
[36]Huber R R,McCulloch B.Self-repairing flight control system.SAE Tech.Paper Series,1984,1-20.
[37]Soloway,Donald,Haley,et al.Reconfigurable flight control using neural generalized predictive control.AIAA Space 2000 Conference and Exposition,Long Beach CA,Sept.19-21,2000.
[38]胡寿松,周川.基于RBF神经网络的模型跟随非线性自修复控制.南京航空航天大学学报,1998,30(5):486-490.
[39]Hang-Ming Liaw,Yeong-May Lin,Kuei-Hsiang Chao.AVSS speed controller with model reference response for induction motor Drive.IEEE Trans.on Industrial Electronics,2001,48(6):1136-1142.
[40]Gao Z,Antsaklis P J.Reconfigurable control systems design via perfect model following.International Journal of Control,1992,56:783-798.
[41]邹益仁,马增良,蒲维编.现场总线控制系统的设计和开发。北京:国防工业出版社,2003.
[42]梅启智.系统可靠性工程基础。科学出版社,1992.
[43]苏晓勤,故障树分析算法改进研究与实现,2005.
[44]T.Song,Y.Shimada,K.Suzuki,H.Sayama Automated Fault Tree Synthesis Using Event Relation Matrix,Journal of the Socitey of Plant Engineers japan,1997,18(4):8-15
[45]申韬,韩守木,黄树红,刘德昌.汽轮发电机组故障诊断的复合式进化网络,中理工大学学报.1998.
[46]杨建刚.人工神经网络实用教程.杭州:浙江大学出版社,2002.
[47]李鹏波,胡德文.系统辨识基础.北京:中国水利水电出版社,2006.
[48]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真.北京:科学出版社,2004.
[49]潘立登,潘仰东.系统辨识与建模.北京:化学工业出版社,2004.
[50]邓自立,王欣,高媛等.建模与估计.北京:科学出版社,2007
[51]F Ding,T.Chen,Combined parameter and output estimation of dual-rate systems using an auxiliary model,Automatica 40(10)(2004)1739-1748
[52]Jie Ding,Feng Ding,Shi Zhang,Parameter identification of multi-input,single-output systems based on FIR models and least squares principle,Applied Mathematics and Computation,Volume 197,Issuel,15 March2008,pages197-305
[53]施吉林,张宏伟,金光日.计算机科学计算.北京:高等教育出版社,2005
[54]魏巍.控制工程工具箱技术手册.北京:国防工业出版社,2004
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