地铁盾构设备状态监测与故障诊断研究
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摘要
在全国许多城市轨道交通建设的蓬勃发展中,盾构挖掘机作为当前隧道施工的主要设备起着越来越重要的作用。由于盾构机是集机、电、液为一体的大型复杂设备,因而及时的分析设备故障产生的原因和正确预报设备潜在的故障信息,从而减少工作过程中因故障而停机的时间,这将会给现代化建设带来明显的经济效益。
文章通过对盾构机故障发生机理的深入研究,广泛收集了故障处理的知识和经验;并结合以知识为基础的常见故障诊断方法,运用专家系统进行盾构故障的智能诊断工作,分析盾构机诊断问题的特点,提出系统的总体方案。本文针对盾构机故障的模糊性特点设计了基于综合动、静态隶属度运算的模糊识别法;针对偶然性和耦合性特点设计了基于贝叶斯原理的概率统计识别法;同时考虑到盾构机状态监测系统所采集的信息是以时间序列数据的形式记录在数据库中的,因此本文引入了时间序列相似性比较的方法,以此实现盾构机故障的预测问题。
论文在研究了上述一系列关键性问题的基础上,以软件工程理论为指导,运用面向对象的程序设计方法和多种数据访问技术,开发了一套以专家系统为基础的盾构机故障诊断系统,该系统具备了模糊推理诊断功能、概率推理诊断功能、实时在线诊断功能,以及设备的状态监测功能。
The vigorous development of rail transport construction in many cities of our country, as the current tunnel construction for major equipment, the TBM is playing an increasingly important role. As the TBM is a large and complex equipment which include machines, electrical and hydraulic, thus a timely analysis of the causes of equipment failure and a correct prediction of potential equipment failure information, thereby reducing the work process downtime due to failure of the time. All of these will bring significant economic benefits.
After deeply studying in the mechanism of TBM, collecting the knowledge and experience about shield fault diagnosis, combining common diagnosis methods based on knowledge, analyzing the features of TBM fault diagnosis, and using expert system as method, a whole system scheme is proposed. This paper presents its own designs of a fuzzy recognition method based upon the comprehensive static and dynamic membership computing to deal with the fuzziness aspect of the object of research,of a probability and statistics method of recognition based upon the Bayesian principle to deal with the aspects of possibility and coupling.
This paper researched a series of key questions, with the guidance of software engineering theory, technology of object-oriented design and two kinds of data accessing technologies, programmed a Inteligent Fault Diagnosis System of TBM based on expert system. The system is able to diagnose interactively and automatically, and also able to diagnose the Status of Device on-line.
文章通过对盾构机故障发生机理的深入研究,广泛收集了故障处理的知识和经验;并结合以知识为基础的常见故障诊断方法,运用专家系统进行盾构故障的智能诊断工作,分析盾构机诊断问题的特点,提出系统的总体方案。本文针对盾构机故障的模糊性特点设计了基于综合动、静态隶属度运算的模糊识别法;针对偶然性和耦合性特点设计了基于贝叶斯原理的概率统计识别法;同时考虑到盾构机状态监测系统所采集的信息是以时间序列数据的形式记录在数据库中的,因此本文引入了时间序列相似性比较的方法,以此实现盾构机故障的预测问题。
论文在研究了上述一系列关键性问题的基础上,以软件工程理论为指导,运用面向对象的程序设计方法和多种数据访问技术,开发了一套以专家系统为基础的盾构机故障诊断系统,该系统具备了模糊推理诊断功能、概率推理诊断功能、实时在线诊断功能,以及设备的状态监测功能。
The vigorous development of rail transport construction in many cities of our country, as the current tunnel construction for major equipment, the TBM is playing an increasingly important role. As the TBM is a large and complex equipment which include machines, electrical and hydraulic, thus a timely analysis of the causes of equipment failure and a correct prediction of potential equipment failure information, thereby reducing the work process downtime due to failure of the time. All of these will bring significant economic benefits.
After deeply studying in the mechanism of TBM, collecting the knowledge and experience about shield fault diagnosis, combining common diagnosis methods based on knowledge, analyzing the features of TBM fault diagnosis, and using expert system as method, a whole system scheme is proposed. This paper presents its own designs of a fuzzy recognition method based upon the comprehensive static and dynamic membership computing to deal with the fuzziness aspect of the object of research,of a probability and statistics method of recognition based upon the Bayesian principle to deal with the aspects of possibility and coupling.
This paper researched a series of key questions, with the guidance of software engineering theory, technology of object-oriented design and two kinds of data accessing technologies, programmed a Inteligent Fault Diagnosis System of TBM based on expert system. The system is able to diagnose interactively and automatically, and also able to diagnose the Status of Device on-line.
引文
[1]张晓丹,赵海,王刚,魏守智.基于信息融合的故障诊断模糊专家系统的应用.吉林大学学报,2004,34(1):141～145
[2]张礼达,余波,王辉等.水电站水轮机故障诊断专家系统构建方法的研究[J].四川水力发电,2001.Vol.20 (S7):93～96
[3] Yu Qian, Xiuxi Li,Yanrong Jiang , et al . An expert system for realtime fClt diagnosis of complex chemical processes [J]. Expert Systems with Applications, 2003.Vol . 25(4):425～432
[4] Teive R C G ,Sliva E L ,Fonseca L G S .A cooperative expert system for transmission expansion of electrical power systems.IEEE Trans on Power Delivery ,1998,13(2):636～642
[5]陈予恕.机械故障诊断的非线性动力学原理[J].机械工程学报,2007.Vol. 43(1):25～34
[6]王成栋,宋鹏举,周健等.TBM故障诊断专家系统的设计与实现.计算机工程与应用.2002,22:14～16
[7]宋鹏举,张优云,董光能.基于Delphi的TBM故障诊断专家系统.机械工程师.2003,1:29～31
[8]王满增,徐明新.TBM故障诊断专家系统的设计与开发.建筑机械. 2003,11:65～69
[9]赵华,苏东,乔文生.TBM主变速箱的状态监测与故障诊断.建筑机械化. 2003,6:44～46
[10]韩亚丽.TBM状态监测与故障诊断.建筑机械.2003,9:30～34
[11]公志波.基于Web的全断面掘进机施工辅助决策系统的研究.武汉:武汉大学,2004
[12]周文波,胡珉.盾构法隧道施工智能化辅助决策系统的研制与应用(下).上海建设科技. 2001,3:17～18
[13]何小新,吴庆鸣.隧道掘进机选型智能决策支持系统的研究.铁道学报.2007,29(3):127～131
[14] Peter E.Sutherland. Performance Calculation for Tunnel Boring Machine Motors. Industry Applications Conference,2000.Conference, 2000.Conference Record of the 2000 IEEE.Vol.4, 2668～2673
[15] Marcelo G..Simoes, Taehong Kim.Fuzzy Modeling Approaches for the Prediction of Machine Utilization in Hard Rock Tunnel Boring Machines.Industry Applications Conference, 2006.41st IAS Annual Meeting .Conference Record of the 2006 IEEE.Vol.2, 947～954
[16] M.Alvarez Grima ,P.A.Bruines , P.N.W.Verhoef. Modeling tunnel Boring Machine Performance by Neuro-Fuzzy Methods .Tunneling and Underground Space Technology.Vol.15,2000,259～269(11)
[17] S Kahraman. Correlation of TBM and drilling machine performances with rock brittleness.Engineering Geology.Vol.67, January 2003, Page 40
[18] G .Swoboda , A.Abu-Krisha .Three-dimensional numerical modeling for TBM tunneling in consolidated clay. Tunneling and Underground Space Technology . Vol.14, 1999, 327～333(7)
[19] S.Okubo, K.Fukui, W.Chen. Expert System for Applicability of Tunnel Boring Machines in Japan. Rock Mechanics and Rock Engineering. Vol.36,2003, 305～322
[20]王守强.盾构施工技术及应用.水利水电科技进展,1997.17(3)
[21]蔡自兴,约翰·德尔金,龚涛.高级专家系统:原理、设计及应用[M].北京:科学出版社,2005
[22]敖志刚.人工智能与专家系统导论[M].合肥:中国科技大学出版社,2002
[23]王永庆.人工智能原理与方法[M].西安:西安交通大学出版社,1998
[24]李毅得.不确定性人工智能[M].北京:国防工业出版社,2005
[25] Buchanan B. G., Heuristic DENDRAL: a program for generating expiator hypotheses in organic chemistry, In Machine Intelligence 4, Edinburgh, Edinburgh University Press, 1969, 209～254
[26] Erman,L.D.etal.The HEARSAY speech-understanding system: integrating knowledge to resolve uncertainty, Computing Surveys, 1980, 12(2): 213～253
[27]许化东.基于故障树分析法的汽车故障诊断专家系统的研究[D].合肥工业大学,2002,4:1～2
[28] Shortliffe, E. H. Computer-Based Medical Consultation: MYCIN, New York, American Elsevier, 1976
[29] Duda, R. O. Development of a computer-base consultant for mineral exploration, Annual Report, SRI Project, 1977,5821
[30]陈照明.汽车制动专家系统知识库的建立及推理机制研究.吉林:吉林大学,2006
[31]李涵武,王伟杰,赵雨旸.故障智能诊断系统的知识表示方法.林业机械与土木设备.2005,5:48
[32] Kohda, T. Ohki, S. Inoue, K. Construction of diagnosis rules from diagnosistable based on error risk criterion. Systems, Man and Cybernetics,1993,12(10):415～420
[33]胡昌华,《控制系统故障诊断与容错控制的分析和设计》,国防工业出版社,2000.7
[34]肖志怀,李朝晖,张志学,闸门综合自动化故障诊断专家系统的知识表达,中国农村水利水电,2004,4:89～92
[35]胡波,张明友,基于故障树的压力容器失效诊断专家系统研究,武汉理工大学学报, 2004,26(2):71～74
[36]王忠祥,郭宝恩,基于数据库的知识表达与推理研究,机械设计与制造,2005,9:152～154
[37]谈理,刘谨,梅丽婷,连续生产线设备故障诊断专家系统的动态模糊推理机制的研究,中国工程科学,2005,7(6): 58～60
[38] GUESSOUM Z, PIERRE J . From active objects to Ctonomous agents[J] . IEEE Concurrency , 1999, 7(3):68～75
[39]谈理,刘瑾,叶赛蓬等.机械设计方案的模糊评估系统的研究[J].机械设计,2006. Vol.23 (4):7～9
[40]阿不都外力.买买提,贝叶斯不确定性推理及其在往复式压缩机故障诊断中的应用,化工装备技术,2005,26(2):65～70
[41]吴新玲,孙凤瑞,金裕红,贝叶斯统计在机电设备故障诊断中的应用,舰船科学技术,2004,26(5):31～33
[42]王道平,张义忠.故障智能诊断系统的理论与方法.北京:冶金工业出版社,2001
[2]张礼达,余波,王辉等.水电站水轮机故障诊断专家系统构建方法的研究[J].四川水力发电,2001.Vol.20 (S7):93～96
[3] Yu Qian, Xiuxi Li,Yanrong Jiang , et al . An expert system for realtime fClt diagnosis of complex chemical processes [J]. Expert Systems with Applications, 2003.Vol . 25(4):425～432
[4] Teive R C G ,Sliva E L ,Fonseca L G S .A cooperative expert system for transmission expansion of electrical power systems.IEEE Trans on Power Delivery ,1998,13(2):636～642
[5]陈予恕.机械故障诊断的非线性动力学原理[J].机械工程学报,2007.Vol. 43(1):25～34
[6]王成栋,宋鹏举,周健等.TBM故障诊断专家系统的设计与实现.计算机工程与应用.2002,22:14～16
[7]宋鹏举,张优云,董光能.基于Delphi的TBM故障诊断专家系统.机械工程师.2003,1:29～31
[8]王满增,徐明新.TBM故障诊断专家系统的设计与开发.建筑机械. 2003,11:65～69
[9]赵华,苏东,乔文生.TBM主变速箱的状态监测与故障诊断.建筑机械化. 2003,6:44～46
[10]韩亚丽.TBM状态监测与故障诊断.建筑机械.2003,9:30～34
[11]公志波.基于Web的全断面掘进机施工辅助决策系统的研究.武汉:武汉大学,2004
[12]周文波,胡珉.盾构法隧道施工智能化辅助决策系统的研制与应用(下).上海建设科技. 2001,3:17～18
[13]何小新,吴庆鸣.隧道掘进机选型智能决策支持系统的研究.铁道学报.2007,29(3):127～131
[14] Peter E.Sutherland. Performance Calculation for Tunnel Boring Machine Motors. Industry Applications Conference,2000.Conference, 2000.Conference Record of the 2000 IEEE.Vol.4, 2668～2673
[15] Marcelo G..Simoes, Taehong Kim.Fuzzy Modeling Approaches for the Prediction of Machine Utilization in Hard Rock Tunnel Boring Machines.Industry Applications Conference, 2006.41st IAS Annual Meeting .Conference Record of the 2006 IEEE.Vol.2, 947～954
[16] M.Alvarez Grima ,P.A.Bruines , P.N.W.Verhoef. Modeling tunnel Boring Machine Performance by Neuro-Fuzzy Methods .Tunneling and Underground Space Technology.Vol.15,2000,259～269(11)
[17] S Kahraman. Correlation of TBM and drilling machine performances with rock brittleness.Engineering Geology.Vol.67, January 2003, Page 40
[18] G .Swoboda , A.Abu-Krisha .Three-dimensional numerical modeling for TBM tunneling in consolidated clay. Tunneling and Underground Space Technology . Vol.14, 1999, 327～333(7)
[19] S.Okubo, K.Fukui, W.Chen. Expert System for Applicability of Tunnel Boring Machines in Japan. Rock Mechanics and Rock Engineering. Vol.36,2003, 305～322
[20]王守强.盾构施工技术及应用.水利水电科技进展,1997.17(3)
[21]蔡自兴,约翰·德尔金,龚涛.高级专家系统:原理、设计及应用[M].北京:科学出版社,2005
[22]敖志刚.人工智能与专家系统导论[M].合肥:中国科技大学出版社,2002
[23]王永庆.人工智能原理与方法[M].西安:西安交通大学出版社,1998
[24]李毅得.不确定性人工智能[M].北京:国防工业出版社,2005
[25] Buchanan B. G., Heuristic DENDRAL: a program for generating expiator hypotheses in organic chemistry, In Machine Intelligence 4, Edinburgh, Edinburgh University Press, 1969, 209～254
[26] Erman,L.D.etal.The HEARSAY speech-understanding system: integrating knowledge to resolve uncertainty, Computing Surveys, 1980, 12(2): 213～253
[27]许化东.基于故障树分析法的汽车故障诊断专家系统的研究[D].合肥工业大学,2002,4:1～2
[28] Shortliffe, E. H. Computer-Based Medical Consultation: MYCIN, New York, American Elsevier, 1976
[29] Duda, R. O. Development of a computer-base consultant for mineral exploration, Annual Report, SRI Project, 1977,5821
[30]陈照明.汽车制动专家系统知识库的建立及推理机制研究.吉林:吉林大学,2006
[31]李涵武,王伟杰,赵雨旸.故障智能诊断系统的知识表示方法.林业机械与土木设备.2005,5:48
[32] Kohda, T. Ohki, S. Inoue, K. Construction of diagnosis rules from diagnosistable based on error risk criterion. Systems, Man and Cybernetics,1993,12(10):415～420
[33]胡昌华,《控制系统故障诊断与容错控制的分析和设计》,国防工业出版社,2000.7
[34]肖志怀,李朝晖,张志学,闸门综合自动化故障诊断专家系统的知识表达,中国农村水利水电,2004,4:89～92
[35]胡波,张明友,基于故障树的压力容器失效诊断专家系统研究,武汉理工大学学报, 2004,26(2):71～74
[36]王忠祥,郭宝恩,基于数据库的知识表达与推理研究,机械设计与制造,2005,9:152～154
[37]谈理,刘谨,梅丽婷,连续生产线设备故障诊断专家系统的动态模糊推理机制的研究,中国工程科学,2005,7(6): 58～60
[38] GUESSOUM Z, PIERRE J . From active objects to Ctonomous agents[J] . IEEE Concurrency , 1999, 7(3):68～75
[39]谈理,刘瑾,叶赛蓬等.机械设计方案的模糊评估系统的研究[J].机械设计,2006. Vol.23 (4):7～9
[40]阿不都外力.买买提,贝叶斯不确定性推理及其在往复式压缩机故障诊断中的应用,化工装备技术,2005,26(2):65～70
[41]吴新玲,孙凤瑞,金裕红,贝叶斯统计在机电设备故障诊断中的应用,舰船科学技术,2004,26(5):31～33
[42]王道平,张义忠.故障智能诊断系统的理论与方法.北京:冶金工业出版社,2001