机车途中故障诊断系统的研究与实现
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摘要
专家系统是一种模拟专家决策能力的计算机系统。论文以机车途中故障诊断为应用背景，利用人工智能、故障诊断的理论和方法，研究并开发了一个机车途中故障诊断专家系统原型，以实现对机车故障的智能诊断。
本论文在研究目前常用的故障诊断智能技术以及人工智能领域中的基于规则推理方法的基础上，给出了基于规则推理和故障树技术的机车途中故障诊断专家系统的设计方案和原型系统。分析和研究了机车故障诊断领域知识特点和大量的故障维修日志，探讨了基于规则推理方法和故障树分析法构造专家系统的关键技术，主要包括基于规则推理的知识工程以及故障树检索技术。
本论文以机车途中故障诊断领域为研究和应用背景，根据机车故障诊断领域的特点以及故障诊断专家系统的应用需求，对产生式规则表示法和故障树分析法等理论、方法和应用技术等一系列问题进行了深入的研究和探讨，主要研究工作有：
1)经过现场的实地调查研究以及在对国内外大量资料的收集、整理、分析、研究的基础上，从应用实践的需求出发，在较高的抽象层次上探讨了机车故障智能诊断的基本内容和特点；并针对机车故障诊断领域的特点，利用混合推理策略和局部最佳优先搜索算法对机车途中故障进行诊断。
2)对产生式规则和故障树技术作了深入的研究和探讨。采用产生式规则表示法的基本原理，借助故障树技术，设计基于规则的故障树表示方法；构造了一个按层次结构组织的多级索引机制的机车故障规则库。并在此基础上，设计了知识库，然后运用混合推理和局部最佳优先搜索算法进行匹配率计算的故障检索机制。
3)基于以上的研究，设计和建立了基于规则推理的机车途中故障诊断专家系统的概念模型和物理模型。在此基础上，用Borland C++Builder5开发完成了软件原型系统。并在江西向塘机务段投入使用，反应良好，验证了设计的合理性和实现的有效性。
最后，通过近一年来该系统在江西向塘机务段的应用实践表明，该机车途中故障诊断系统是切实可行的。
Expert system is a computer simulating systems of the expert decision-making ability. This Paper taking locomotive fault on the railway diagnosis as application background, using artificial intelligence, fault diagnosis theory and methods, studied and developed a diagnostic system of locomotive fault on the railway to achieve to the locomotive fault intelligent diagnosis.
The paper produced design proposal and the prototype system of diagnose expert system of the locomotive fault on the railway based on the RBR and the Fault-tree technology, on the base of studying the fault intelligent diagnosis technology at present in commonly used and the RBR method in artificial intelligence domain. The paper analyzed and studied the knowledge characteristic of the locomotive fault diagnosis domain and massive fault service diary. And the paper discussed essential technology of structuring expert system based on the RBR method and the Fault-tree analytic method, which mainly includes the knowledge engineering of the RBR and the Fault-tree retrieval technology.
The paper taking locomotive fault diagnosis domain as research and application background, according to the locomotive fault diagnosis domain characteristic and application demand of fault diagnosis expert system, studied thoroughly a series of questions such as the RBA method and the Fault-tree analytic method, and which main research work includes following several.
After scene on-the-spot investigation research and on the basis of collecting, collating and anglicizing a large number of domestic and foreign information, according to application demand of the practice, discussed basic content and the characteristic of the locomotive fault intelligence diagnosis in a higher abstract level, and using mix reason strategy and partial best-first search algorithm to diagnose locomotive fault.
The paper researched and discussed thoroughly the production pattern rule and the Fault-tree technology. Using the basic principle of the production pattern regular method of portrayal, with the aid of Fault-tree technology, designed the RBR Fault-tree expression method.
Designed and established the conceptual model and the physical model of the locomotive fault diagnosed expert system. And put it to use in the Xiangtang locomotive depot in Jiangxi province, get a good appraisal, and which confirmed the design rationality and the realization validity.
本论文在研究目前常用的故障诊断智能技术以及人工智能领域中的基于规则推理方法的基础上，给出了基于规则推理和故障树技术的机车途中故障诊断专家系统的设计方案和原型系统。分析和研究了机车故障诊断领域知识特点和大量的故障维修日志，探讨了基于规则推理方法和故障树分析法构造专家系统的关键技术，主要包括基于规则推理的知识工程以及故障树检索技术。
本论文以机车途中故障诊断领域为研究和应用背景，根据机车故障诊断领域的特点以及故障诊断专家系统的应用需求，对产生式规则表示法和故障树分析法等理论、方法和应用技术等一系列问题进行了深入的研究和探讨，主要研究工作有：
1)经过现场的实地调查研究以及在对国内外大量资料的收集、整理、分析、研究的基础上，从应用实践的需求出发，在较高的抽象层次上探讨了机车故障智能诊断的基本内容和特点；并针对机车故障诊断领域的特点，利用混合推理策略和局部最佳优先搜索算法对机车途中故障进行诊断。
2)对产生式规则和故障树技术作了深入的研究和探讨。采用产生式规则表示法的基本原理，借助故障树技术，设计基于规则的故障树表示方法；构造了一个按层次结构组织的多级索引机制的机车故障规则库。并在此基础上，设计了知识库，然后运用混合推理和局部最佳优先搜索算法进行匹配率计算的故障检索机制。
3)基于以上的研究，设计和建立了基于规则推理的机车途中故障诊断专家系统的概念模型和物理模型。在此基础上，用Borland C++Builder5开发完成了软件原型系统。并在江西向塘机务段投入使用，反应良好，验证了设计的合理性和实现的有效性。
最后，通过近一年来该系统在江西向塘机务段的应用实践表明，该机车途中故障诊断系统是切实可行的。
Expert system is a computer simulating systems of the expert decision-making ability. This Paper taking locomotive fault on the railway diagnosis as application background, using artificial intelligence, fault diagnosis theory and methods, studied and developed a diagnostic system of locomotive fault on the railway to achieve to the locomotive fault intelligent diagnosis.
The paper produced design proposal and the prototype system of diagnose expert system of the locomotive fault on the railway based on the RBR and the Fault-tree technology, on the base of studying the fault intelligent diagnosis technology at present in commonly used and the RBR method in artificial intelligence domain. The paper analyzed and studied the knowledge characteristic of the locomotive fault diagnosis domain and massive fault service diary. And the paper discussed essential technology of structuring expert system based on the RBR method and the Fault-tree analytic method, which mainly includes the knowledge engineering of the RBR and the Fault-tree retrieval technology.
The paper taking locomotive fault diagnosis domain as research and application background, according to the locomotive fault diagnosis domain characteristic and application demand of fault diagnosis expert system, studied thoroughly a series of questions such as the RBA method and the Fault-tree analytic method, and which main research work includes following several.
After scene on-the-spot investigation research and on the basis of collecting, collating and anglicizing a large number of domestic and foreign information, according to application demand of the practice, discussed basic content and the characteristic of the locomotive fault intelligence diagnosis in a higher abstract level, and using mix reason strategy and partial best-first search algorithm to diagnose locomotive fault.
The paper researched and discussed thoroughly the production pattern rule and the Fault-tree technology. Using the basic principle of the production pattern regular method of portrayal, with the aid of Fault-tree technology, designed the RBR Fault-tree expression method.
Designed and established the conceptual model and the physical model of the locomotive fault diagnosed expert system. And put it to use in the Xiangtang locomotive depot in Jiangxi province, get a good appraisal, and which confirmed the design rationality and the realization validity.
引文
[1] 李一鹏．机车故障诊断系统：[硕士学位论文]。陕西：西北工业大学，2006．3-5
[2] 朱剑峰．8k电力机车中央柜故障诊断系统的研究与应用：[硕士学位论文]．湖南：中南大学，2005．13-16
[3] 代建华，陈卫东，潘云鹤．基于粗糙集的综合推理模型[J]．浙江大学学报(工学版)，2006．9
[4] 周桂法，陈特放．机车在线故障诊断专家系统的研究[J]．长沙铁道学院学报，2002．20(1)
[5] 徐小力，王涛．转子系统故障推理智能专家系统的研究与实现[J]．计量与测试技术，2004．8
[6] 田宏萍，张家栋．机车电器电路故障诊断系统的研究[J]．内燃机车，2000．10
[7] 朱大奇，于盛林．基于知识的故障诊断方法综述[J]．安徽工业大学学报，2002．7
[8] 夏勇等．基于图像处理与神经网络的内燃机故障诊断研究[J]．内燃机学报，2001．19(4)
[9] 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，2000．145-156
[10] 齐怡．军用电子设备故障诊断专家系统研究[D]。北京：北京航空航天大学，2003．12-15
[11] 袁洪芳，江志农．DIS故障诊断专家系统知识获取子系统的开发[J]．北京化工人学学报，2002．4
[12] 苏群星，解璞，贾长治．基于自动测试系统的故障诊断专家系统[J]．计算机测量与控制，2003．11
[13] 曾儒伟，许诚等．故障诊断方法发展动向[J]．航空计算技术，2003．9
[14] 陈战林，张万里，耿宏运等．C++ Builder组件大全[M]．北京：电子工业出版社，2002．255-230
[15] 丁福焰，杜永平．机车车辆故障诊断技术的发展[J]．北京：铁道机车车辆，2004．24(4)
[16] 蔡泽样，俞亮，高爱云．大型电动机的内部故障诊断与保护方法[J]．电力自动化设备，2002．7
[17] 樊文侠，王安，何娇娜．基于机车故障诊断的电路图形的动态显示的研究[J]．科技广场，2005．12
[18] 李一鹏，王安．一种新的机车电路动态显示系统[J]．陕西科技大学学报，2005．3
[19] 杨宗志．C++ Builder数据库程序设计[M]．北京：清华大学出版社，2001．187-201
[20] 卢学军，缪思恩等．故障诊断专家系统知识处理及知识库管理方法研究[J]．计算机工程与应用，2001．24
[21] 陈灿煌．C++ Builder 6彻底研究．北京：中国铁道出版社，2003．122-145
[22] (美)Herbert Schildt，Greg Guntle著；周海斌…[等]译．C++ Builder技术大全[M]．北京：机械工业出版社，2002．151-169
[23] 余明兴．Borland C++ Builder 6程序设计经典[M]．北京：科学出版社，2004．113-125
[24] 刘光．趣味程序导学C++ Builder[M]．北京：清华大学出版社，2002．56-78
[25] (美)Lance Mortensen，Rick Sawtell，Joseph L．Jorden著；邱仲潘等译．MCSE：SQL Server 2000 Administration学习指南[M]．北京：电子工业出版社，2001．77-82
[26] (美)Jeffrey Bane，Anil Desai，Craig Robinson著；袁鹏飞译．MCDBA学习指南：SQL Server 2000数据库设计[M]：Exam 70-229．北京：人民邮电出版社，2002．66-87
[27] Ken Henderson著：康博译．SQL Server存储过程、XML和HTML高级指南[M]．北京：清华大学出版社，2002．133-157
[28] (美)微软公司著；张长富…[等]译．Microsoft SQL Server 2000数据库编程[M]．北京：北京希望电子出版社，2001．122-134
[29] 尹朝庆等．人工智能与专家系统[M]．水利水电出版社，2002．5-23
[30] 丁青艳，张秀媛．铁路信号故障诊断专家系统基本结构设计[J]．铁路计算机应用．2005．5
[29] William G Blevins, Richard Morgan, Chris Pinney. Integrated Wayside Systems to Improve Safety and Efficiency [A]. In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings[C]. May 5-9,2003, Dallas, Texas, U.S.A., 4.61-4. 70
[30] Curt A. Swenson. Remote Monitoring and Diagnostics for Improving Locomotive Availability and Utilization [A). In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings [C], May S-9,2003, Dallas, Texas, U.S.A., 4.71-4.78
[31] Firdausi D. Irani, R.B. Wiley, Thomas S. Guins. Network Efficiency Gains Using a Detector-based Information Management System [A]. In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings [C]. May S-9, 2003. Dallas, Texas, U.S.A., 4.87-4.92
[32] L Heng-Jac, P Deung-Yong, A Bok-Shin. A fuzzy expert system for the integrated fault diagnosis[J].IEEE Transactions on Power Delivery, 2000.15(2):833-838
[33] Cheng shu, et al. Expert System of Fault Diagnosis Based on the Neural Network and Regulations[C]. The Third World Congress on Intelligent Control and Automation, 2000.327-329.
[34] Liu Shulin,et al. Negative-selection Algorithm Based Approach for Fault Diagnosis of Rotary Machinery. American Control Conference, Proceedings of the 2002, 2002.5
[35] Heung-Jae Lee, Bok-ShinAhn, Young-Moon Park. A Fault Diagnosis Expert System for Distribution Substations . IEEE Transactions on Power Delivery. No. 1, January 2000.92-97
[36] S.M.A. Cruz, A. J. M. Cardoso, H. A.Toliyat. Diagnosis of Stator, Rotor and Airgap Eccentricity Faults in Three-Phase Induction Motors Based on the Multiple Reference Frames Theory. IEEE, 2003:1340-1346
[37] Curt A. Swenson. Remote Monitoring and Diagnostics for Improving Locomotive Availability and Utilization [A]. In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings [C]. May 5-9,2003, Dallas, Texas, U.S.A., 4.71-4.78
[38] Martin T.Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design. Proceedings AAAI-91, Anaheim, CA, 2002. 212-275
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[40] H. B. Jensen, R.Wisniewski. Fault Detection and Isolation for Spacecraft Geometric Approach. AIAA 2002-4955
[41] J. J. Fox . Informed Maintenance for Next Generation Reusable Launch Systems. Acta Astronautica. 2001, 48(5-12): 439-449
[42] G. .B. Aaseng. Blueprint for an Integrated Vehicle Health Management System. Digital Avionics Systems, 20th Conference, Oct. 2001:14-18
[2] 朱剑峰．8k电力机车中央柜故障诊断系统的研究与应用：[硕士学位论文]．湖南：中南大学，2005．13-16
[3] 代建华，陈卫东，潘云鹤．基于粗糙集的综合推理模型[J]．浙江大学学报(工学版)，2006．9
[4] 周桂法，陈特放．机车在线故障诊断专家系统的研究[J]．长沙铁道学院学报，2002．20(1)
[5] 徐小力，王涛．转子系统故障推理智能专家系统的研究与实现[J]．计量与测试技术，2004．8
[6] 田宏萍，张家栋．机车电器电路故障诊断系统的研究[J]．内燃机车，2000．10
[7] 朱大奇，于盛林．基于知识的故障诊断方法综述[J]．安徽工业大学学报，2002．7
[8] 夏勇等．基于图像处理与神经网络的内燃机故障诊断研究[J]．内燃机学报，2001．19(4)
[9] 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，2000．145-156
[10] 齐怡．军用电子设备故障诊断专家系统研究[D]。北京：北京航空航天大学，2003．12-15
[11] 袁洪芳，江志农．DIS故障诊断专家系统知识获取子系统的开发[J]．北京化工人学学报，2002．4
[12] 苏群星，解璞，贾长治．基于自动测试系统的故障诊断专家系统[J]．计算机测量与控制，2003．11
[13] 曾儒伟，许诚等．故障诊断方法发展动向[J]．航空计算技术，2003．9
[14] 陈战林，张万里，耿宏运等．C++ Builder组件大全[M]．北京：电子工业出版社，2002．255-230
[15] 丁福焰，杜永平．机车车辆故障诊断技术的发展[J]．北京：铁道机车车辆，2004．24(4)
[16] 蔡泽样，俞亮，高爱云．大型电动机的内部故障诊断与保护方法[J]．电力自动化设备，2002．7
[17] 樊文侠，王安，何娇娜．基于机车故障诊断的电路图形的动态显示的研究[J]．科技广场，2005．12
[18] 李一鹏，王安．一种新的机车电路动态显示系统[J]．陕西科技大学学报，2005．3
[19] 杨宗志．C++ Builder数据库程序设计[M]．北京：清华大学出版社，2001．187-201
[20] 卢学军，缪思恩等．故障诊断专家系统知识处理及知识库管理方法研究[J]．计算机工程与应用，2001．24
[21] 陈灿煌．C++ Builder 6彻底研究．北京：中国铁道出版社，2003．122-145
[22] (美)Herbert Schildt，Greg Guntle著；周海斌…[等]译．C++ Builder技术大全[M]．北京：机械工业出版社，2002．151-169
[23] 余明兴．Borland C++ Builder 6程序设计经典[M]．北京：科学出版社，2004．113-125
[24] 刘光．趣味程序导学C++ Builder[M]．北京：清华大学出版社，2002．56-78
[25] (美)Lance Mortensen，Rick Sawtell，Joseph L．Jorden著；邱仲潘等译．MCSE：SQL Server 2000 Administration学习指南[M]．北京：电子工业出版社，2001．77-82
[26] (美)Jeffrey Bane，Anil Desai，Craig Robinson著；袁鹏飞译．MCDBA学习指南：SQL Server 2000数据库设计[M]：Exam 70-229．北京：人民邮电出版社，2002．66-87
[27] Ken Henderson著：康博译．SQL Server存储过程、XML和HTML高级指南[M]．北京：清华大学出版社，2002．133-157
[28] (美)微软公司著；张长富…[等]译．Microsoft SQL Server 2000数据库编程[M]．北京：北京希望电子出版社，2001．122-134
[29] 尹朝庆等．人工智能与专家系统[M]．水利水电出版社，2002．5-23
[30] 丁青艳，张秀媛．铁路信号故障诊断专家系统基本结构设计[J]．铁路计算机应用．2005．5
[29] William G Blevins, Richard Morgan, Chris Pinney. Integrated Wayside Systems to Improve Safety and Efficiency [A]. In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings[C]. May 5-9,2003, Dallas, Texas, U.S.A., 4.61-4. 70
[30] Curt A. Swenson. Remote Monitoring and Diagnostics for Improving Locomotive Availability and Utilization [A). In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings [C], May S-9,2003, Dallas, Texas, U.S.A., 4.71-4.78
[31] Firdausi D. Irani, R.B. Wiley, Thomas S. Guins. Network Efficiency Gains Using a Detector-based Information Management System [A]. In: International Heavy Haul Association 2003 Specialist Technical Session Proceedings [C]. May S-9, 2003. Dallas, Texas, U.S.A., 4.87-4.92
[32] L Heng-Jac, P Deung-Yong, A Bok-Shin. A fuzzy expert system for the integrated fault diagnosis[J].IEEE Transactions on Power Delivery, 2000.15(2):833-838
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[34] Liu Shulin,et al. Negative-selection Algorithm Based Approach for Fault Diagnosis of Rotary Machinery. American Control Conference, Proceedings of the 2002, 2002.5
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