拉弯机液压系统故障诊断研究
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摘要
拉弯机是生产加工飞机上的框肋缘条等型材零件的必须设备，常用于制造尺寸大、外形准确度要求较高、相对弯曲半径大的挤压和板弯型材弯曲件。随着拉弯机数字化程度的日益提高，拉弯机自身的故障诊断技术已成为其关键技术之一。液压系统作为拉弯机的核心，研究其故障诊断技术，对于保证加工零件的质量、提高拉弯机的工作可靠性和安全性更是意义重大。
本文深入研究了转台式拉弯机液压系统的故障诊断技术，建立了转台式拉弯机液压系统故障诊断的层次模型，提出了相应的故障诊断的策略。在详细分析了转台式拉弯机液压系统的组成结构、工艺特点和故障形式及特点的基础上，确立了液压系统状态监测的内容，组建了系统测试的硬件平台。通过对系统状态信号的测试、特征分析和提取技术研究，为最终实现状态监测、故障诊断及预报奠定了基础。利用UML技术建立了系统的软件模型，采用智能故障诊断的专家系统技术与方法，用VC++工具进行了系统开发并通过实验方法对系统的可靠性进行了分析。
Stretch-bending former is used to manufacture curved pieces with large size and high shaped precision. Generally, these pieces are relatively large winding radius extrusion or sheet bending profiled bars. Especially, stretch-bending former is the needed equipment to produce rims, ribs, edges and strips of airplanes. With its digital degree advanced increasingly, self-fault diagnosis technique has been one of key techniques to stretch-bending former system. Hydraulic system is the kernel to stretch-bending former. So it's even most important to study of the hydraulic system fault diagnosis technology. Only can these improve the reliability and security of stretch-bending former as well as quality of machining parts.
This thesis investigates stretch-bending former hydraulic system fault diagnosis in detail, analyzing the former constitutes and process characteristics, researching fault patterns and features of the former. And strategies for rotary stretch-bending former hydraulic system fault diagnosis are brought forward. And a leveled systemic fault diagnosis model is established lastly. By laboring stretch-bending former system function demands, its condition monitoring contents and hardware testing platform are established. Also, fault signals acquisition, examination and process methods are researched as well. All of these settle a good groundwork for monitoring, predicting and diagnosing faults. Stretch-bending former intelligent hydraulic fault diagnosis system is developed in virtue of expert system theory and method. With software model established by UML (The Unified Modeling Language) technique and using Visual C++ 6.0 tool, the monitoring and fault diagnosis system is developed and fulfilled. In the end, the system reliability is validated by test.
本文深入研究了转台式拉弯机液压系统的故障诊断技术，建立了转台式拉弯机液压系统故障诊断的层次模型，提出了相应的故障诊断的策略。在详细分析了转台式拉弯机液压系统的组成结构、工艺特点和故障形式及特点的基础上，确立了液压系统状态监测的内容，组建了系统测试的硬件平台。通过对系统状态信号的测试、特征分析和提取技术研究，为最终实现状态监测、故障诊断及预报奠定了基础。利用UML技术建立了系统的软件模型，采用智能故障诊断的专家系统技术与方法，用VC++工具进行了系统开发并通过实验方法对系统的可靠性进行了分析。
Stretch-bending former is used to manufacture curved pieces with large size and high shaped precision. Generally, these pieces are relatively large winding radius extrusion or sheet bending profiled bars. Especially, stretch-bending former is the needed equipment to produce rims, ribs, edges and strips of airplanes. With its digital degree advanced increasingly, self-fault diagnosis technique has been one of key techniques to stretch-bending former system. Hydraulic system is the kernel to stretch-bending former. So it's even most important to study of the hydraulic system fault diagnosis technology. Only can these improve the reliability and security of stretch-bending former as well as quality of machining parts.
This thesis investigates stretch-bending former hydraulic system fault diagnosis in detail, analyzing the former constitutes and process characteristics, researching fault patterns and features of the former. And strategies for rotary stretch-bending former hydraulic system fault diagnosis are brought forward. And a leveled systemic fault diagnosis model is established lastly. By laboring stretch-bending former system function demands, its condition monitoring contents and hardware testing platform are established. Also, fault signals acquisition, examination and process methods are researched as well. All of these settle a good groundwork for monitoring, predicting and diagnosing faults. Stretch-bending former intelligent hydraulic fault diagnosis system is developed in virtue of expert system theory and method. With software model established by UML (The Unified Modeling Language) technique and using Visual C++ 6.0 tool, the monitoring and fault diagnosis system is developed and fulfilled. In the end, the system reliability is validated by test.
引文
[1] Edward Youcolon等著．殷人昆等译．实用面向对象软件工程教程[M]．PRENTICE HALL出版公司，1998．
[2] 刘水健，胡培金，液压故障诊断分析[M]．北京：人民交通出版社，1998．
[3] 夺寿萱．板金成形原理与工艺．西安：西北工业大学出版社，1985．
[4] 陈章位，路甬祥等．液压设备状态监测和故障诊断技术[J]．液压与气动，1995(2)：3～7．
[5] RM.Frank,B.Koppen-Seliger, New developments using AI in fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 1997,10(1):3～14.
[6] 陈章位．电液伺服系统故障诊断专家系统的研究[博士学位论文]．杭州：浙江大学，1994．
[7] 黄志坚．液压设备故障分析与技术改进[M]．武汉：华中理工大学出版社，1999．
[8] 李国华，张永忠．机械故障诊断[M]．北京：化学工业出版社，1999．
[9] 钟秉林，黄仁．机械故障诊断学[M]．北京：机械工业出版社，1997．
[10] 陈兆能，余经洪．液压设备状态监测与故障诊断[M]．上海：上海科学技术文献出版社，1997．
[11] 吴今培，肖健华．智能故障诊断与专家系统[M]．北京：科学出版社，1997．9
[12] 谢涛．基于进化计算的液体火箭发动机故障诊断技术研究[D]．国防科技大学博士学位论文，1998．
[13] 贵忠华，刘振凯．智能混合系统研究综述[J]．信息与控制，2000，29(1)．
[14] 周东华，孙优贤．控制系统的故障检测与诊断技术[M]．北京：清华大学出版社，1994．
[15] ZHANG R, ZHU R, CAI L. Study on on-line Fault Diagnosis Intelligence for a Submersible Pump[J].Chinese Journal of Mechanical Engineering, 200137(4).
[16] 王益群，高英杰．液压AGC系统故障诊断专家系统的实现[J]．液压气动与密封，2000(1)．
[17] 黄志坚，陈长征，张省等．液压故障智能诊断技术探讨与展望[J]．冶金自动化，2001(3)
[18] 张建华．液压系统状态监测及故障诊断研究[博士学位沦文]．北京：北京航空航天大学，1996．
[19] 陈安华，钟掘．机械故障诊断技术进展[J]．湘潭矿业学院学报，1996．11(1)
[20] 邝朴生等．现代机器故障诊断学[M]．北京：农业出版社，1991．
[21] 周东华，叶银忠．现代故障诊断与容错控制[M]．北京：清华大学出版社，2000．
[22] Ge W, Fang C Z.Detection of fault components via robust observation[J]. Int J Control, 1988,47(2).
[23] 张育林等．动态系统的故障诊断理沦与应用[M]．长沙：国防科技大学出版社，1997．
[24] 胡昌华，许化龙．控制系统故障诊断于容错控制的分析与设计[M]．北京：国防工业出版社，2000．
[25] Frank P M.Analytical and qualitative model-based fault diagnosis-a survey and some new results[J].European J of Control, 1996,2(1).
[26] Magni J F,Mouyon P.On residual generation by observer and parity apace approaches [J].IEEE Trans on Autornatic Control, 1994,39(2).
[27] Frank P M.Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection system [J].J Process Control, 1997,7(6).
[28] Chen J,Patton R J.Robust model-based fault diagnosis for dynamic systems[M]. Norwell:Kluwer Academic Publisher, 1999.
[29] Sauter D,Hamelin F.Frequency-domain optimization for robust fault detection and isolation in dynamic systems[J].IEEE Trans on Automatic Control, 1999,44(4).
[30] Nyberg M,Frisk E.A minimal polynomial basis solution to residual generation for fault diagnosis in linear systems [C].In :Proc of 14th IFAC World Congress,Beijing,1999.
[31] Chen J C,Lopez-Toribio J,Patton R J.Non-linear dynamic systems fault detection and isolation using fuzzy observers[J].Proc Instn Mech Engrs, 1999,213(1).
[32] Koscielny J M.Application of fuzzy logic for fault isolation in a three tank system [C].In:Proc of 14th IFAC World Congress,Beijing. 1999.
[33] 刘建设．机械设备液压系统故障诊断技术的现状与展望[J]．现代机械，2003(2)．
[34] 马霄，王文深．液压系统故障诊断专家系统的设计[J]．组合机床与自动化加工技术，2003(8)．
[35] 郭华伟，胡军科等．液压故障诊断专家系统研究[J]．机床与液压，2002(6)．
[36] 韩邦华，鲍红书，李光霞．面向对象的故障诊断专家系统[J]．机械设计与制造，2003(3)．
[37] 范永海，齐铁力．液压系统可维修性设计研究．液压与气动，2003(8)．
[2] 刘水健，胡培金，液压故障诊断分析[M]．北京：人民交通出版社，1998．
[3] 夺寿萱．板金成形原理与工艺．西安：西北工业大学出版社，1985．
[4] 陈章位，路甬祥等．液压设备状态监测和故障诊断技术[J]．液压与气动，1995(2)：3～7．
[5] RM.Frank,B.Koppen-Seliger, New developments using AI in fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 1997,10(1):3～14.
[6] 陈章位．电液伺服系统故障诊断专家系统的研究[博士学位论文]．杭州：浙江大学，1994．
[7] 黄志坚．液压设备故障分析与技术改进[M]．武汉：华中理工大学出版社，1999．
[8] 李国华，张永忠．机械故障诊断[M]．北京：化学工业出版社，1999．
[9] 钟秉林，黄仁．机械故障诊断学[M]．北京：机械工业出版社，1997．
[10] 陈兆能，余经洪．液压设备状态监测与故障诊断[M]．上海：上海科学技术文献出版社，1997．
[11] 吴今培，肖健华．智能故障诊断与专家系统[M]．北京：科学出版社，1997．9
[12] 谢涛．基于进化计算的液体火箭发动机故障诊断技术研究[D]．国防科技大学博士学位论文，1998．
[13] 贵忠华，刘振凯．智能混合系统研究综述[J]．信息与控制，2000，29(1)．
[14] 周东华，孙优贤．控制系统的故障检测与诊断技术[M]．北京：清华大学出版社，1994．
[15] ZHANG R, ZHU R, CAI L. Study on on-line Fault Diagnosis Intelligence for a Submersible Pump[J].Chinese Journal of Mechanical Engineering, 200137(4).
[16] 王益群，高英杰．液压AGC系统故障诊断专家系统的实现[J]．液压气动与密封，2000(1)．
[17] 黄志坚，陈长征，张省等．液压故障智能诊断技术探讨与展望[J]．冶金自动化，2001(3)
[18] 张建华．液压系统状态监测及故障诊断研究[博士学位沦文]．北京：北京航空航天大学，1996．
[19] 陈安华，钟掘．机械故障诊断技术进展[J]．湘潭矿业学院学报，1996．11(1)
[20] 邝朴生等．现代机器故障诊断学[M]．北京：农业出版社，1991．
[21] 周东华，叶银忠．现代故障诊断与容错控制[M]．北京：清华大学出版社，2000．
[22] Ge W, Fang C Z.Detection of fault components via robust observation[J]. Int J Control, 1988,47(2).
[23] 张育林等．动态系统的故障诊断理沦与应用[M]．长沙：国防科技大学出版社，1997．
[24] 胡昌华，许化龙．控制系统故障诊断于容错控制的分析与设计[M]．北京：国防工业出版社，2000．
[25] Frank P M.Analytical and qualitative model-based fault diagnosis-a survey and some new results[J].European J of Control, 1996,2(1).
[26] Magni J F,Mouyon P.On residual generation by observer and parity apace approaches [J].IEEE Trans on Autornatic Control, 1994,39(2).
[27] Frank P M.Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection system [J].J Process Control, 1997,7(6).
[28] Chen J,Patton R J.Robust model-based fault diagnosis for dynamic systems[M]. Norwell:Kluwer Academic Publisher, 1999.
[29] Sauter D,Hamelin F.Frequency-domain optimization for robust fault detection and isolation in dynamic systems[J].IEEE Trans on Automatic Control, 1999,44(4).
[30] Nyberg M,Frisk E.A minimal polynomial basis solution to residual generation for fault diagnosis in linear systems [C].In :Proc of 14th IFAC World Congress,Beijing,1999.
[31] Chen J C,Lopez-Toribio J,Patton R J.Non-linear dynamic systems fault detection and isolation using fuzzy observers[J].Proc Instn Mech Engrs, 1999,213(1).
[32] Koscielny J M.Application of fuzzy logic for fault isolation in a three tank system [C].In:Proc of 14th IFAC World Congress,Beijing. 1999.
[33] 刘建设．机械设备液压系统故障诊断技术的现状与展望[J]．现代机械，2003(2)．
[34] 马霄，王文深．液压系统故障诊断专家系统的设计[J]．组合机床与自动化加工技术，2003(8)．
[35] 郭华伟，胡军科等．液压故障诊断专家系统研究[J]．机床与液压，2002(6)．
[36] 韩邦华，鲍红书，李光霞．面向对象的故障诊断专家系统[J]．机械设计与制造，2003(3)．
[37] 范永海，齐铁力．液压系统可维修性设计研究．液压与气动，2003(8)．
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