大型制氧系统的故障诊断方法与系统开发研究
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摘要
制氧系统是工业生产中不可或缺的工艺系统,它的主要功能是为工业生产提供氧气和氮气,以满足工业生产对氧气的需要。伴随着制氧系统设备的大型化、自动化、复杂化,制氧设备的故障诊断也变得十分复杂。因此故障监测和故障诊断在制氧系统的运行维护中的作用越来越受到重视。
制氧设备的故障具有渐变性、不确定性的特点,为了能够准确、可靠地判断出设备故障必须采用合理的故障诊断算法对多传感器数据进行处理,从而判断出正确的设备故障模式。本文深入介绍了故障诊断的各种方法,对于各种方法的优点与缺点进行了深刻的剖析。同时介绍了数据融合的基本概念、基本原理,以及数据融合的层次和模型结构形式,对常用的数据融合算法进行了总结归类,并详细介绍了D-S证据理论和模糊积分的理论基础以及算法实现。在此基础之上提出将模糊数学、D-S证据理论和模糊积分算法应用于制氧系统的设备故障诊断当中,并将三种方法结合起来应用于制氧系统的重要设备-主换热器的故障诊断中；对于D-S证据理论在应用过程中可能由于证据冲突等原因而造成数据融合错误的情况提出了解决方法。通过算例证明由模糊数学、D-S证据理论和模糊积分算法所构成的故障诊断能够提高故障诊断可靠性与准确性。
本文介绍了目前国内开发故障诊断系统的方式、方法,指出其在现场应用中的缺点。提出利用可编程控制器(PLC)的上位组态软件开发故障诊断系统的思想,通过国产组态软件-组态王实现了基于模糊数学、D-S证据理论和模糊积分算法的C语言制氧设备故障诊断程序,通过测试得到了满意的结果,证明通过PLC的上位组态软件进行故障诊断系统开发具有现实可行性,并且该方法简单、省钱、省力、省时,能够很好地将故障诊断技术应用于实际当中。
The oxygen-making systems are indispensable in the industry production. The function is supplying the oxygen and nitrogen which is necessary for the industrial producing process. The fault diagnosis is becoming fully complicated because the oxygen-making equipments are becoming larger, more complex and automatic. The fault detection and fault diagnosis of the oxygen-making systems are attached more importance nowadays.
The fault characteristics of the oxygen-making equipment are time delay and uncertainty. If we want to have a right equipment fault model, we must take the right algorithms to process the multi-sensor data, and that way must to be accurate. This paper presents a variety of the fault diagnosis methods, and the advantages and the disadvantages of all of these methods are discussed. This paper also introduces the basic principle and concept of data fusion, summarizes levels and model structure of data fusion, summarizes common algorithms and sorts them, and separately expounds D-S evidence theory and fuzzy integral, which includes the theoretical basis and algorithm realizing. Father the paper puts forward applying the fuzzy mathematics, D-S evidence theory and fuzzy integral to do the fault diagnosis work for the oxygen-making equipments. In the paper,the three algorithms are applied to the main heat exchanger fault diagnosis which belong to the oxygen-making system. It also presents the way that can solve the problem of D-S evidence theory fusion mistake in the applying process due to the evidence confliction. It indicates by simulation that the fault diagnosis method which includes fuzzy mathematics, D-S evidence theory and fuzzy integral can improve the reliability of the equipment fault diagnosis and can increase accuracy of diagnosis.
The paper introduces the way of developing the fault diagnosis system in China, and points out the disadvantages of the way, then puts forward applying the PLC configuration software of the upper PC to do the fault diagnosis system development work for the oxygen-making system, the fault diagnosis algorithm are the fuzzy mathematics, D-S evidential theory and fuzzy integral. It also designs the main heat exchanger fault diagnosis program using the King View. It indicates by simulation that the method is possible.
制氧设备的故障具有渐变性、不确定性的特点,为了能够准确、可靠地判断出设备故障必须采用合理的故障诊断算法对多传感器数据进行处理,从而判断出正确的设备故障模式。本文深入介绍了故障诊断的各种方法,对于各种方法的优点与缺点进行了深刻的剖析。同时介绍了数据融合的基本概念、基本原理,以及数据融合的层次和模型结构形式,对常用的数据融合算法进行了总结归类,并详细介绍了D-S证据理论和模糊积分的理论基础以及算法实现。在此基础之上提出将模糊数学、D-S证据理论和模糊积分算法应用于制氧系统的设备故障诊断当中,并将三种方法结合起来应用于制氧系统的重要设备-主换热器的故障诊断中；对于D-S证据理论在应用过程中可能由于证据冲突等原因而造成数据融合错误的情况提出了解决方法。通过算例证明由模糊数学、D-S证据理论和模糊积分算法所构成的故障诊断能够提高故障诊断可靠性与准确性。
本文介绍了目前国内开发故障诊断系统的方式、方法,指出其在现场应用中的缺点。提出利用可编程控制器(PLC)的上位组态软件开发故障诊断系统的思想,通过国产组态软件-组态王实现了基于模糊数学、D-S证据理论和模糊积分算法的C语言制氧设备故障诊断程序,通过测试得到了满意的结果,证明通过PLC的上位组态软件进行故障诊断系统开发具有现实可行性,并且该方法简单、省钱、省力、省时,能够很好地将故障诊断技术应用于实际当中。
The oxygen-making systems are indispensable in the industry production. The function is supplying the oxygen and nitrogen which is necessary for the industrial producing process. The fault diagnosis is becoming fully complicated because the oxygen-making equipments are becoming larger, more complex and automatic. The fault detection and fault diagnosis of the oxygen-making systems are attached more importance nowadays.
The fault characteristics of the oxygen-making equipment are time delay and uncertainty. If we want to have a right equipment fault model, we must take the right algorithms to process the multi-sensor data, and that way must to be accurate. This paper presents a variety of the fault diagnosis methods, and the advantages and the disadvantages of all of these methods are discussed. This paper also introduces the basic principle and concept of data fusion, summarizes levels and model structure of data fusion, summarizes common algorithms and sorts them, and separately expounds D-S evidence theory and fuzzy integral, which includes the theoretical basis and algorithm realizing. Father the paper puts forward applying the fuzzy mathematics, D-S evidence theory and fuzzy integral to do the fault diagnosis work for the oxygen-making equipments. In the paper,the three algorithms are applied to the main heat exchanger fault diagnosis which belong to the oxygen-making system. It also presents the way that can solve the problem of D-S evidence theory fusion mistake in the applying process due to the evidence confliction. It indicates by simulation that the fault diagnosis method which includes fuzzy mathematics, D-S evidence theory and fuzzy integral can improve the reliability of the equipment fault diagnosis and can increase accuracy of diagnosis.
The paper introduces the way of developing the fault diagnosis system in China, and points out the disadvantages of the way, then puts forward applying the PLC configuration software of the upper PC to do the fault diagnosis system development work for the oxygen-making system, the fault diagnosis algorithm are the fuzzy mathematics, D-S evidential theory and fuzzy integral. It also designs the main heat exchanger fault diagnosis program using the King View. It indicates by simulation that the method is possible.
引文
[1]徐智求.大型空分系统的远程故障诊断专家系统的开发[D].湖北：华中科技大学,2006
[2]黄雄武.低温系统故障诊断专家系统-膨胀机模块的开发与应用[D].湖北：华中科技大学,2006
[3]苏羽,赵海等.一种基于模糊神经网络的融合故障诊断方法[J].计算机工程,2004,130(17)：56-60
[4]张建华,王占林.基于模糊神经网络的故障诊断方法的研究[J].北京航空航天大学学报,1997,23(4)：78-85
[5]张雨,徐小林,张建华.设备状态监测与故障诊断理论与实践[M].北京：北京国防科技大学出版社,2000
[6]周鸣岐,徐军.控制系统故障诊断[J].计算机自动测量与控制,2000,8(3)：5-8
[7]Magin J F and Mouyon P. On residual generation by observer and parity space approaches [J]. IEEETransaction on Automatic Control,1994,39(2)441-447
[8]Garcia E A, Frank P M. on the relationship between observer and parameter identification based approaches to fault detection [J]. Proc.of IFAC World Congress,1996:25-29
[9]Gertler J. Diagnosing parametric faults: from param-eter estimation to parity relation[J]. American Con-trol Conference,1995:1615-1620
[10]周东华,孙优贤.控制系统故障检测与诊断技术[M].北京：清华大学出版社,1994
[11]周东华,孙优贤,席裕庚等.一类非线性系统参数偏差型故障的实时检测与诊断[J].自动化学报,1993,19(2)：184-189
[12]Isermann R. Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper[J].Automatica.1993,29(4):815-835
[13]Zhou D H, Frank P M. Fault Diagnostics and Fault Tolerant Control[J]. IEEE Trans. on Aerospace and Electronic Systems,1998,34(2):420-427
[14]周东华.三相感应电动机的在线故障检测与诊断问题研究[J].控制理论与应用,1995,12(1)：58-63
[15]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
[16]Ding S X ,Jeinsch T. Application of observer based FDI schemes to the three tank system[C].Proc. of European Control Conference, Karlsruhe,Germang,1999
[17]Kumamaru K, et al. Robust fault detection using in-dex of Kullback discrimination information[J].In:Proc. of IFAC World Congress,1996.205-210
[18]叶昊,王贵增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6)：736-741
[19]叶昊,王贵增,方崇智等.一种基于小波变换的导弹运输车辆故障诊断方法[J].自动化学报,1998,24(3)：301-306
[20]张育林,李东旭.动态系统故障诊断理论与应用[M].长沙：国防科技大学出版社,1997
[21]孙涛,宏建.目标识别中的信息融合技术[J].自动化仪表,,2001,22(2)：1-4
[22]刘俊,付替奇,董新平.数据融合在目标识别中的应用[J].传感器技术,2001,20(6)：8-11
[23]康规红.数据融合原理与应用[M].西安：西安电子科技大学出版社,1997：3-30
[24]刘同明,夏祖勋.数据融合技术[M].北京：国防工业出版社,1998：1-57
[25]潘震中.多传感器信息融合的谢弗-登普斯特方法[J].无线电通信技术,1995,21(6)：22-31
[26]Grossmann P.Multisensor Data Fusion.The GEC Journal of Technology, Vol. I5,No.1,1998
[27]David L.H.Introduction to Multisensor Data Fusion.Proceedings of the IEEE,Vol.85,No.1,1997,6-23
[28]Richardson J.M.Fusion of Multisensor Data.The International Journal of Robotics Research, Vol.7,No.6,1988, p78-95
[29]潘震中.数据融合的关键技术[J].情报指挥控制与仿真技术,1994(5)：16-22
[30]涂小强.信息融合的原理与方法[J].电讯技术,1999(3)：1-6
[31]P. J. Escamilla-Ambrosio, N. Mort. A hybrid Kalman filter-fuzzy logic architecture for multisensor data fusion.IEEE International Symposium on Intelligent.Control-Proceedings,2001,(9):364-369
[32]Biao Chen, Pramod K. Varshney.A Bayesian Sampling Approach to Decision Fusion Using Hierarchical Models.IEEE Transactions on Signal Processing,2002,50(8):1809-1818
[33]Behzad Moshiri, Mohammad Reza Asharif, Reza HoseinNezhad. Pseudo Information Measure: a New Concept for Extension of Bayesian Fusion in Robotic Map Building.Information Fusion,2002,(3):51-68
[34]蓝金辉,马宝华,蓝天等.D-S证据理论数据融合方法在目标识别中的应用[J].清华大学学报(自然科学版),2001,41(2)：53-55,59
[35]韩静,陶云刚.基于D-S证据理论和模糊数学的多传感器数据融合算法[J].仪器仪表学报2000,21(6)：644-647
[36]刘怀国,吴陈,张冰.D-S证据理论在多传感器融合中的应用[J].华东船舶工业学院学报(自然科学版),2001,15(3)：20-24
[37]黄瑛,陶云刚,周洁民.D-S证据理论在多传感器数据融合中的应用[J].南京航空航天大学学报,1999,31(4)：172-177
[38]倪国强,梁好臣.基于Dempster-Shafer证据理论的数据融合技术研究[J].北京理工大学学报,2001,21(5)：603-609
[39]罗志增,蒋静坪.相关证据的融合及其在机器人多感觉信息融合中的应用[J].传感器学报,2000(3)：177-182
[40]焦李成.神经网络系统理论[M].西安：西安电子科技大学,1990：12-32
[41]袁曾任.人工神经网络及其应用[M].北京：清华大学出版社1999：14-25 66-274
[42]徐从富,耿卫东,谢澎等.面向通侦信息融合的模糊神经网络方法[J].计算机研究与发展,2000,37(10)：1212-1217
[43]Xia Youshen, Leung Henry,Bosse Eloi.Neural data fusion algorithms based on a linearly constrained least square method. IEEE Transactionson Neural Networks,2002,13(2):320-329
[44]Chris J. Harris, Qiang Gam.State estimation and multi-sensor data fusion using data-based neruofuzzy local linearization process models,2001(2):17-29
[45]Shang liang chen, Y W. Jen. Data fusion Neural Network for Tool Condition Monitoring in CNC Milling Machining. International journal of Machine Tools&Manufacture,2000,40:381-400
[46]李玉榕,蒋静坪,杨富文.基于神经网络的D-S证据理论应用于多传感器目标识别[J].仪器仪表学报,2001,22(6)：652-655
[47]Thierry Denver. A Neural Network Classifier Base on Dempster-Shafer Theory.IEEE Transactions on Systems,Man, and Cybernetics-Part A:Systems and Humans,2000,30(2):131-150
[48]陆正刚,杨洁.模糊逻辑及其在数据融合中的应用[J].红外与激光工程,2000,29(5)：56-59
[49]Sansanee Auephanweriyakul, James M. Keller, Paul D. Gader. Generalized Choquer Fuzzy Integral Fusion. Information Fusion,2002,(3):69-85
[50]Y M. Chen, H. C. Huang. Fuzzy Logic Approach to Multisensor Data Association.Mathematics and Computers in Simulation,2000,52:399-41
[51]战兴群,吴盛林,赵克定等.基于人工智能的液压系统故障诊断方法的研究[J].机床与液压,1997(6)：6-8
[52]C. Angeli, A. Chatzinikolaou. An Expert System Approach to Fault Diagnosis in Hydraulic Systems. Expert System,1995,12(4):323-328
[53]高英杰.轧机AGC液压系统故障诊断技术的研究：[工学博士论文].河北：燕山大学,2001：5-14
[54]高翔.液压泵的状态监测与故障诊断[J].海军工程学院学报,1994(8)：76-87
[55]A..P.Dempster. Upper and Lower Probabilities Induced by a Multivalued Mapping. Annals of Mathematical Statistics.1967,38:325-339
[56]A.P.Dempster. A Generalization of Bayesian Infeence. Journal of the Royal Statistical Society.1968,30:205-247
[57]Liu L J,Yang J Y.Model-based object classification using fused data.SPIE 1991,1611:65-73
[58]李凡.模糊专家系统[M].湖北：华中理工大学出版社,1995
[59]Fabrizio R., Fuzzy Methods for Multisensor Data Fusion, IEEE Transactions on Instrumentation and Measurement.Vol.43, No.2,1994
[60]梁继民.多传感器融合方法研究：[博士毕业论文].陕西：西安电子科技大学,1999：19-20.
[61]Zhenyuan Wang, George J.Klir.Fuzzy Measure Theory. Plenum Press,1992: 46-47,62-63
[62]张文修.模糊数学基础[M].陕西：西安交通大学出版社,1984
[63]哈明虎,吴从釿.模糊测度与模糊积分理论[M].北京：科学出版社,1998
[64]汤学忠,顾福民.新编制氧工问答[M].北京：冶金工业出版社,2001,100-126.
[65]钟晓龙,陈刚.20000m3/h制氧系统主换热器泄漏分析及处理[J].深冷技术,2006(1)：47-50.
[66]李文龙.40000m3/h制氧系统主换热器泄漏分析与处理[J].深冷技术,2006,(1)：51-53.
[67]宋莉.基于PLC的远程监控与故障诊断[D].山东：山东科技大学.2004.
[68]刘小敏.基于Internet的异构故障诊断系统集成研究[D].江苏：苏州大学.2007.
[69]王艳.旋转机械状态监测与故障诊断系统的开发[D].云南：昆明理工大学.2007.
[70]赵海鸣等.旋转机械在线状态监测与故障诊断系统[J].机电工程,2006,23(8)：15-17.
[71]孙长飞.旋转机械在线监测与诊断系统[J].机电产品开发与创新,2005,18(2)：80-
[2]黄雄武.低温系统故障诊断专家系统-膨胀机模块的开发与应用[D].湖北：华中科技大学,2006
[3]苏羽,赵海等.一种基于模糊神经网络的融合故障诊断方法[J].计算机工程,2004,130(17)：56-60
[4]张建华,王占林.基于模糊神经网络的故障诊断方法的研究[J].北京航空航天大学学报,1997,23(4)：78-85
[5]张雨,徐小林,张建华.设备状态监测与故障诊断理论与实践[M].北京：北京国防科技大学出版社,2000
[6]周鸣岐,徐军.控制系统故障诊断[J].计算机自动测量与控制,2000,8(3)：5-8
[7]Magin J F and Mouyon P. On residual generation by observer and parity space approaches [J]. IEEETransaction on Automatic Control,1994,39(2)441-447
[8]Garcia E A, Frank P M. on the relationship between observer and parameter identification based approaches to fault detection [J]. Proc.of IFAC World Congress,1996:25-29
[9]Gertler J. Diagnosing parametric faults: from param-eter estimation to parity relation[J]. American Con-trol Conference,1995:1615-1620
[10]周东华,孙优贤.控制系统故障检测与诊断技术[M].北京：清华大学出版社,1994
[11]周东华,孙优贤,席裕庚等.一类非线性系统参数偏差型故障的实时检测与诊断[J].自动化学报,1993,19(2)：184-189
[12]Isermann R. Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper[J].Automatica.1993,29(4):815-835
[13]Zhou D H, Frank P M. Fault Diagnostics and Fault Tolerant Control[J]. IEEE Trans. on Aerospace and Electronic Systems,1998,34(2):420-427
[14]周东华.三相感应电动机的在线故障检测与诊断问题研究[J].控制理论与应用,1995,12(1)：58-63
[15]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
[16]Ding S X ,Jeinsch T. Application of observer based FDI schemes to the three tank system[C].Proc. of European Control Conference, Karlsruhe,Germang,1999
[17]Kumamaru K, et al. Robust fault detection using in-dex of Kullback discrimination information[J].In:Proc. of IFAC World Congress,1996.205-210
[18]叶昊,王贵增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6)：736-741
[19]叶昊,王贵增,方崇智等.一种基于小波变换的导弹运输车辆故障诊断方法[J].自动化学报,1998,24(3)：301-306
[20]张育林,李东旭.动态系统故障诊断理论与应用[M].长沙：国防科技大学出版社,1997
[21]孙涛,宏建.目标识别中的信息融合技术[J].自动化仪表,,2001,22(2)：1-4
[22]刘俊,付替奇,董新平.数据融合在目标识别中的应用[J].传感器技术,2001,20(6)：8-11
[23]康规红.数据融合原理与应用[M].西安：西安电子科技大学出版社,1997：3-30
[24]刘同明,夏祖勋.数据融合技术[M].北京：国防工业出版社,1998：1-57
[25]潘震中.多传感器信息融合的谢弗-登普斯特方法[J].无线电通信技术,1995,21(6)：22-31
[26]Grossmann P.Multisensor Data Fusion.The GEC Journal of Technology, Vol. I5,No.1,1998
[27]David L.H.Introduction to Multisensor Data Fusion.Proceedings of the IEEE,Vol.85,No.1,1997,6-23
[28]Richardson J.M.Fusion of Multisensor Data.The International Journal of Robotics Research, Vol.7,No.6,1988, p78-95
[29]潘震中.数据融合的关键技术[J].情报指挥控制与仿真技术,1994(5)：16-22
[30]涂小强.信息融合的原理与方法[J].电讯技术,1999(3)：1-6
[31]P. J. Escamilla-Ambrosio, N. Mort. A hybrid Kalman filter-fuzzy logic architecture for multisensor data fusion.IEEE International Symposium on Intelligent.Control-Proceedings,2001,(9):364-369
[32]Biao Chen, Pramod K. Varshney.A Bayesian Sampling Approach to Decision Fusion Using Hierarchical Models.IEEE Transactions on Signal Processing,2002,50(8):1809-1818
[33]Behzad Moshiri, Mohammad Reza Asharif, Reza HoseinNezhad. Pseudo Information Measure: a New Concept for Extension of Bayesian Fusion in Robotic Map Building.Information Fusion,2002,(3):51-68
[34]蓝金辉,马宝华,蓝天等.D-S证据理论数据融合方法在目标识别中的应用[J].清华大学学报(自然科学版),2001,41(2)：53-55,59
[35]韩静,陶云刚.基于D-S证据理论和模糊数学的多传感器数据融合算法[J].仪器仪表学报2000,21(6)：644-647
[36]刘怀国,吴陈,张冰.D-S证据理论在多传感器融合中的应用[J].华东船舶工业学院学报(自然科学版),2001,15(3)：20-24
[37]黄瑛,陶云刚,周洁民.D-S证据理论在多传感器数据融合中的应用[J].南京航空航天大学学报,1999,31(4)：172-177
[38]倪国强,梁好臣.基于Dempster-Shafer证据理论的数据融合技术研究[J].北京理工大学学报,2001,21(5)：603-609
[39]罗志增,蒋静坪.相关证据的融合及其在机器人多感觉信息融合中的应用[J].传感器学报,2000(3)：177-182
[40]焦李成.神经网络系统理论[M].西安：西安电子科技大学,1990：12-32
[41]袁曾任.人工神经网络及其应用[M].北京：清华大学出版社1999：14-25 66-274
[42]徐从富,耿卫东,谢澎等.面向通侦信息融合的模糊神经网络方法[J].计算机研究与发展,2000,37(10)：1212-1217
[43]Xia Youshen, Leung Henry,Bosse Eloi.Neural data fusion algorithms based on a linearly constrained least square method. IEEE Transactionson Neural Networks,2002,13(2):320-329
[44]Chris J. Harris, Qiang Gam.State estimation and multi-sensor data fusion using data-based neruofuzzy local linearization process models,2001(2):17-29
[45]Shang liang chen, Y W. Jen. Data fusion Neural Network for Tool Condition Monitoring in CNC Milling Machining. International journal of Machine Tools&Manufacture,2000,40:381-400
[46]李玉榕,蒋静坪,杨富文.基于神经网络的D-S证据理论应用于多传感器目标识别[J].仪器仪表学报,2001,22(6)：652-655
[47]Thierry Denver. A Neural Network Classifier Base on Dempster-Shafer Theory.IEEE Transactions on Systems,Man, and Cybernetics-Part A:Systems and Humans,2000,30(2):131-150
[48]陆正刚,杨洁.模糊逻辑及其在数据融合中的应用[J].红外与激光工程,2000,29(5)：56-59
[49]Sansanee Auephanweriyakul, James M. Keller, Paul D. Gader. Generalized Choquer Fuzzy Integral Fusion. Information Fusion,2002,(3):69-85
[50]Y M. Chen, H. C. Huang. Fuzzy Logic Approach to Multisensor Data Association.Mathematics and Computers in Simulation,2000,52:399-41
[51]战兴群,吴盛林,赵克定等.基于人工智能的液压系统故障诊断方法的研究[J].机床与液压,1997(6)：6-8
[52]C. Angeli, A. Chatzinikolaou. An Expert System Approach to Fault Diagnosis in Hydraulic Systems. Expert System,1995,12(4):323-328
[53]高英杰.轧机AGC液压系统故障诊断技术的研究：[工学博士论文].河北：燕山大学,2001：5-14
[54]高翔.液压泵的状态监测与故障诊断[J].海军工程学院学报,1994(8)：76-87
[55]A..P.Dempster. Upper and Lower Probabilities Induced by a Multivalued Mapping. Annals of Mathematical Statistics.1967,38:325-339
[56]A.P.Dempster. A Generalization of Bayesian Infeence. Journal of the Royal Statistical Society.1968,30:205-247
[57]Liu L J,Yang J Y.Model-based object classification using fused data.SPIE 1991,1611:65-73
[58]李凡.模糊专家系统[M].湖北：华中理工大学出版社,1995
[59]Fabrizio R., Fuzzy Methods for Multisensor Data Fusion, IEEE Transactions on Instrumentation and Measurement.Vol.43, No.2,1994
[60]梁继民.多传感器融合方法研究：[博士毕业论文].陕西：西安电子科技大学,1999：19-20.
[61]Zhenyuan Wang, George J.Klir.Fuzzy Measure Theory. Plenum Press,1992: 46-47,62-63
[62]张文修.模糊数学基础[M].陕西：西安交通大学出版社,1984
[63]哈明虎,吴从釿.模糊测度与模糊积分理论[M].北京：科学出版社,1998
[64]汤学忠,顾福民.新编制氧工问答[M].北京：冶金工业出版社,2001,100-126.
[65]钟晓龙,陈刚.20000m3/h制氧系统主换热器泄漏分析及处理[J].深冷技术,2006(1)：47-50.
[66]李文龙.40000m3/h制氧系统主换热器泄漏分析与处理[J].深冷技术,2006,(1)：51-53.
[67]宋莉.基于PLC的远程监控与故障诊断[D].山东：山东科技大学.2004.
[68]刘小敏.基于Internet的异构故障诊断系统集成研究[D].江苏：苏州大学.2007.
[69]王艳.旋转机械状态监测与故障诊断系统的开发[D].云南：昆明理工大学.2007.
[70]赵海鸣等.旋转机械在线状态监测与故障诊断系统[J].机电工程,2006,23(8)：15-17.
[71]孙长飞.旋转机械在线监测与诊断系统[J].机电产品开发与创新,2005,18(2)：80-