制冷机组故障检测与诊断研究
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摘要
HVAC&R系统的能耗在国民经济中的能耗占了相当大的比重,特别是服务于公共建筑的大型制冷机组,是现代建筑的主要能耗终端。制冷机组存在故障时,会造成制冷机组的性能下降,同时将降低建筑用户的舒适性水平。因此,对商业使用的大型水冷式制冷机组进行故障检测与诊断,并使制冷机组优化运行、控制于正常工作状态具有重要的意义。本文的目标就是开发一种稳健的、基于物理定律的制冷机组故障检测与诊断方法,从而使复杂的制冷系统不需要增加新的传感器而使用机组本身的传感器就可以进行故障检测与故障诊断。
本文首先对大型水冷式制冷机组的故障情况进行了调查,通过多种方法,对制冷各部件、各组件的故障进行了详细的调研,并使用统计学原理,对制冷机组各部件的故障率、使用寿命等进行了分析,得到了各故障的分布规律。
其次,通过对制冷机组工作过程的合理简化,在热力学第一、第二定律等物理定律的基础上,采用特征参数与故障因子的方法,概括、提取了制冷机组发生故障时的参数特征,建立了大型蒸汽压缩式制冷机组故障检测与诊断的静态模型。为使建立的模型能够检测和诊断制冷机组实际运行过程中所发生的故障,对所采集的动态数据进行了处理。
为检验制冷机组故障检测与诊断模型的正确性和有效性,运用所建立的数学模型,开发了制冷机组故障检测与诊断工具,对制冷机组故障检测工具进行了总体设计和软件编程,并对这个模型进行了校验。运用所建立的模型和诊断工具,对来自于现场的实验数据进行了分析。以HXC165A制冷机组为例,分析了各种故障条件下的性能变化,通过对比故障条件和健康条件下各特性参数的变化规律,得到了各种故障的报警条件和极限停机条件。因此,故障检测与诊断过程中所进行的参数监控也对优化制冷机组的运行具有重要的意义。
最后,运用可靠性理论,对制冷机组的可靠性指标进行了计算,对故障修复进行了分析,重点对故障检测与诊断的经济性进行了分析,从而证明了制冷机组故障检测与诊断的巨大意义,使本文的逻辑性、连贯性得到了更好的体现。
The energy consumption of heating,ventilation,air-conditioning and refrigerating(HVAC&R)systems plays a significant role in national economy. Especially,the large-scale chiller plants,which serve for modern public buildings, are the chief energy consumption terminals.When the chiller plants are existence of faults,it will decline the coefficient of performance(COP),and also the comfort levels for building occupants.Therefore,it is essential for large-scale business operated water-cooling chiller plants to carry out fault detection and diagnosis(FDD) and make them optimization conditions.The objective of this research work is to develop a robust physical law model-based FDD methodology for chiller plants. Consequently,the complicated refrigeration system can carry out FDD as much as possible on using existing sensors,tranducers and other hardware rather than requiring those installed specially for this purpose.
The faults survey for large-scale water-cooling chiller plants are obtained at first in the paper.The fault occurrence of chiller plants components are surveyed through fully field investigation,and then the faults rates and lifetimes are analyzed with statistical theory,therefore,the faults distribution rules are acquired.
Secondly,based on the thermodynamic first and second laws,the faults characteristic parameters(CQs)and faults indexs(FIs)are generalized and drawn by reasonable simplification chiller plants operation process.Therefore,the static state FDD models are established for the large-scale evapor compressed chiller plants combination the CQs and FIs.The practical dynamic operation data are pretreated so as the established static state models can detect and diagnose the chiller plants faults during the real operation conditions.
Thirdly,the FDD tool has been developed with modeled mathematic models in order to verify the preciseness and validity of the chiller plants.The FDD tool has been programed and verified with established models.The field experiment data with large-scale chiller plants are utilized to analyze FDD tool.The performance diversifications under different fault conditions are analyzed with HXC165A as example.By comparison the rules of characteristic parameters under the fault and fault-free conditions,the threshold value of alarm and termination conditions have been obtained as well.Therefore,the parameters monitoring during the FDD is also significant for the chiller plants performance optimization.
Lastly,the faults modification and reliability indexes are discussed and analyzed with reliability theory.Especially,the economic effect during the FDD for chiller plants are analyzed as well,which argues the grand meaning of FDD,and also bring the paper better logicality and continuity.
本文首先对大型水冷式制冷机组的故障情况进行了调查,通过多种方法,对制冷各部件、各组件的故障进行了详细的调研,并使用统计学原理,对制冷机组各部件的故障率、使用寿命等进行了分析,得到了各故障的分布规律。
其次,通过对制冷机组工作过程的合理简化,在热力学第一、第二定律等物理定律的基础上,采用特征参数与故障因子的方法,概括、提取了制冷机组发生故障时的参数特征,建立了大型蒸汽压缩式制冷机组故障检测与诊断的静态模型。为使建立的模型能够检测和诊断制冷机组实际运行过程中所发生的故障,对所采集的动态数据进行了处理。
为检验制冷机组故障检测与诊断模型的正确性和有效性,运用所建立的数学模型,开发了制冷机组故障检测与诊断工具,对制冷机组故障检测工具进行了总体设计和软件编程,并对这个模型进行了校验。运用所建立的模型和诊断工具,对来自于现场的实验数据进行了分析。以HXC165A制冷机组为例,分析了各种故障条件下的性能变化,通过对比故障条件和健康条件下各特性参数的变化规律,得到了各种故障的报警条件和极限停机条件。因此,故障检测与诊断过程中所进行的参数监控也对优化制冷机组的运行具有重要的意义。
最后,运用可靠性理论,对制冷机组的可靠性指标进行了计算,对故障修复进行了分析,重点对故障检测与诊断的经济性进行了分析,从而证明了制冷机组故障检测与诊断的巨大意义,使本文的逻辑性、连贯性得到了更好的体现。
The energy consumption of heating,ventilation,air-conditioning and refrigerating(HVAC&R)systems plays a significant role in national economy. Especially,the large-scale chiller plants,which serve for modern public buildings, are the chief energy consumption terminals.When the chiller plants are existence of faults,it will decline the coefficient of performance(COP),and also the comfort levels for building occupants.Therefore,it is essential for large-scale business operated water-cooling chiller plants to carry out fault detection and diagnosis(FDD) and make them optimization conditions.The objective of this research work is to develop a robust physical law model-based FDD methodology for chiller plants. Consequently,the complicated refrigeration system can carry out FDD as much as possible on using existing sensors,tranducers and other hardware rather than requiring those installed specially for this purpose.
The faults survey for large-scale water-cooling chiller plants are obtained at first in the paper.The fault occurrence of chiller plants components are surveyed through fully field investigation,and then the faults rates and lifetimes are analyzed with statistical theory,therefore,the faults distribution rules are acquired.
Secondly,based on the thermodynamic first and second laws,the faults characteristic parameters(CQs)and faults indexs(FIs)are generalized and drawn by reasonable simplification chiller plants operation process.Therefore,the static state FDD models are established for the large-scale evapor compressed chiller plants combination the CQs and FIs.The practical dynamic operation data are pretreated so as the established static state models can detect and diagnose the chiller plants faults during the real operation conditions.
Thirdly,the FDD tool has been developed with modeled mathematic models in order to verify the preciseness and validity of the chiller plants.The FDD tool has been programed and verified with established models.The field experiment data with large-scale chiller plants are utilized to analyze FDD tool.The performance diversifications under different fault conditions are analyzed with HXC165A as example.By comparison the rules of characteristic parameters under the fault and fault-free conditions,the threshold value of alarm and termination conditions have been obtained as well.Therefore,the parameters monitoring during the FDD is also significant for the chiller plants performance optimization.
Lastly,the faults modification and reliability indexes are discussed and analyzed with reliability theory.Especially,the economic effect during the FDD for chiller plants are analyzed as well,which argues the grand meaning of FDD,and also bring the paper better logicality and continuity.
引文
[1][1]大型制冷机组的冲洗与调试.建筑热能通风空调,2006,25(4):95-99
[1]亚热带地区建筑能源效率与节能.广东工业大学学报,2006,23(1):1-8
[3]暖通空调系统故障检测与诊断研究进展.暖通空调,2005,35(12):31-38
[1]陆亚俊,马最良,邹平华.暖通空调.北京:中国建筑工业出版社,2002,6-8
[2]马增林.中国制冷机组和大型空调设备的发展趋势.http://www.gzrd.com,2005-12-08
[3]龙惟定,白玮.我国民用建筑空调的发展前景.http://www.hvacinf.com,2005-11-10
[4]齐智,王勋陵.激光对蚕豆幼苗紫外线—B辐射损伤的防护作用.中国激光,2002,29(1):91-94
[5]Yingxin Zhu.Sustainable housing and urban construction in China.Energy and Buildings,2004,36(12):1289-1297
[6]Paul Crompton,Yanrui Wu.Energy consumption in China:past trends and future directions.Energy Economics,(2005),27(1):195-208
[7]John phelan,Michael Brandmuehl,Moncf Krart.Review of laboratory and field methods to measure fan,pump and chiller performance.ASHRAE Trans,1997,103(2),914-925
[8]House JM,Lee WY,Shin DR.Classification techniques for fault detection and diagnosis of air-hanlding unit.ASHRAE Transactions,1999,105(1):1087-1097
[9]Arthur.D,Jouko Pakanen.Annex 34 computer Aided Evaluation of HVAC System Performance.In:Proceedings of VTT Symposium.Finland,2001,13-19
[10]Todd M Rossi,James E Braun.Minimizing operating costs of vapor compression equipment with optimal service scheduling.Int.J of heating,ventilation,air-conditioning and refrigerating research,1996,2(1):3-26
[11]Katipamula S,Braubley M.Automated diagnostics improving building system and equipment performance.Energy user news,1998,23(4):1454-1462
[12]Yoshida H,T Iwami,H Yuzawa,et al.Typical faults of air-conditioning system and fault detection by ARX model and extended Kalman filter.ASHRAE Transactions,1996,102(1):557-564
[13]J.A.Giles,W.Ishida,world market for air-conditioning update 2001.http://www.bisra.co.uk.2005-06-04
[14]The Australian Greenhouse Office,commercial building air conditioning chillers.http://www.energyrating.gov.au.2004-04-06
[15]BSRIA Worldwide Market Intelligence.World Market for Air Conditioning 2002-entire study.London:BSRIA Limited,2003,1-46
[16] The Australian Greenhouse Office, commercial building air conditioning chillers. http://www.energyrating.gov.au/library/pubs/200415-mepschillers.pdf. 2004-04-06
[17] Ghiaus C. Fault diagnosis of air conditioning systems based on qualitative bond graph. Energy and Buildings, 1999,30(3): 221-232
[18] Haves P, T Salsbury, J A Wright. Condition monitoring in HVAC subsystems using first principles. ASHRAE Transactions, 1996, 102(1): 519-527
[19] Comstock M C, J E Braun, E A Groll. A survey of common faults in chillers. ASHRAE Transactions, 2002, 108(1): 819-825
[20] Hydeman M, N Webb, P Sreedharan et al. Development and testing of a reformulated regression-based electric chiller model. ASHRAE Transactions, 2002, 108(2): 1118-1127
[21] Michele B. Detecting changes in signals and systems-A survey. Automatic, 1988, 24(3): 309-326
[22] Frank P. M. Fault diagnosis systems using analytical and knowledge-based redundancy-A survey and some new results. Automation, 1990, 26(3): 459-474
[23] Janes. J. Gertler. Survey of model-based failure detection and isolation in complex plants. IEEE control system magazine, 1988, 8(6): 3-11
[24] Rolf. Isermann. Process fault detection based on modeling estimation methods-A survey. Automatica, 1988, 20(4): 384-407
[25] Alans. Willsky. A survey of design methods for failure detection in dynamic systems. Automatica, 1976, 12 (6): 601-611
[26] Rossi T. M., J. E. Braun. A Statistical rule-based fault detection and diagnostic method for vapor compression air conditioner. Int. J. of HVAC&R Research, 1997,3(1): 19-37
[27] Stylianou M, D Nikanpour. Performance monitoring, fault detection, and diagnosis of reciprocating chillers. ASHRAE Transactions, 1996, 102(1): 615-627.
[28] Breuker M. S, J. E. Braun. Common faults and their impacts for rooftop air conditioners. Int. J. of HVAC&R Research, 1998, 4(3): 303-308
[29] D. Bultman, L. Burmeister, V. Bortone et al. Vapor-compression refrigerator performance degradation due to condenser air flow blockage. America Society of Mechanical Engineers, 1993,93(4): 1-13
[30] Breuker M.S, J.E.Braun. Common faults and their impacts for rooftop air conditioners. Int. J. of HVAC&Research, 1998, 4(3):303-308
[31]Srinivas Katipamula,Michael R.Brambley.Methods for Fault Detection,Diagnostics,and Prognostics for Building Systems—A Review,Part Ⅰ.HVAC&R RESEARCH,2005,11(1):1-24
[32]Breuker M.S.,J.E.Braun.Evaluating the performance of a fault detection and diagnostics system for vapor compression equipment.Int.J.of HVAC&R Research,1998,4(4):401-425
[33]Stouppe D.E,Y.S.Lau.Air conditioning and refrigeration equipment failures.National Engineer,1989,93(9):14-17.
[34]Mekellar M.G.Fault diagnosis for a household refrigerator:[Master thesis].School of mechanical engineering.Indiana:Purdue University,1987,25-41
[35]Stallard L A.Model based expert system of failure detection and identification household refrigerators:[Master's thesis].Indiana:School of mechanical engineering,Purdue University,1989,28-75
[36]Yoshimura M,N.Ito.Effective diagnosis methods for air-conditioning equipment in communication buildings.In:Proceeding of IEEE INTELEC 89.Firenze,1989,1-17
[37]Kumamaru T,Utsunomiya T,Yamada Y.A fault diagnosis system for district heating and cooling facilities.In:Proceeding of International Electronics,Control and Instrumentation,Japan,1991,131-136
[38]Peitsman,H and V.Bakker.Application of Black-Box models to HVAC systems for fault detection.ASHRAE Transactions,1996,103(2):914-925
[39]Bailey M B.The design and viability of a probabilistic fault detection and diagnosis method for vapor compression cycle equipment:[PH.D thesis].Colorado:School of civil engineering,University of Colorado,1998,11-34
[40]Wagner J,Shoureshi.Fault detection diagnosis for thermofluid systems.Journal of dynamic systems,measurement and control,1992,114(4):699-706
[41]H.T.Grimmelius,J.Klein Woud,G Been.On-line failure diagnosis for compression refrigeration plants.Int.J.of Refrigeration,1995,18(1):31-41
[42]蔡立群.制冷系统故障诊断的专家系统:[上海交通大学硕士论文].上海:上海交通大学机械系,1993,24-45
[43]赵鹏.模糊专家系统在制冷系统在线故障诊断中的实现应用:[上海交通大学硕士论文].上海:上海交通大学机械系,1995,37-51
[44]鲍士雄,赵鹏.制冷系统故障诊断中模糊模式识别技术的应用.制冷学报,1998.20(2):20-27
[45]晋欣桥.冷水机组系统的温度传感器故障诊断.上海交通大学学报,2004, 38(1):976-981
[46]姜大勇,黄道.空气处理单元的故障检测与诊断方法.建筑热能通风空调,1999,18(2):106-108
[47]赵黎丽.两种人工智能方法应用于地热热泵系统辨识.系统仿真学报,2004,16(7):1376-1379
[48]李玉云.人工神经网络在暖通空调领域的应用研究发展.暖通空调,2001,31(1):77-79
[49]张振军.中央空调系统现场检测方法初探.制冷与空调,2003,3(4):24-26
[50]胡昶.基于温度检测和神经网络的空调负荷预测.仪器仪表学报,2003,24(1):427-428
[51]何书森.热泵干燥过程PID加Fuzzy调温的应用与实验.农业机械学报,1999,43(1):25-29
[52]刘宪英.神经网络法负荷预测及在蓄冰空调系统的优化运行.重庆建筑大学学报,1999,43(6):86-90
[53]刘贤坤.基于人工神经网络的变风量HVAC系统热力过程优化.四川制冷与空调,2003,19(2):1-5
[54]李韵.YCAM-H-600热泵故障分析及其对策.暖通空调,2003,33(2):102-104
[55]姜益强,姚扬,马最良.基于人工神经网络的空气源热泵冷热水机组的性能模拟.流体机械,2002,30(5):59-61
[56]姜益强,姚扬,马最良.空气源热泵冷水机组的故障诊断.制冷学报,2002,23(3):58-61
[57]陈友明,郝小礼.建筑能源管理与控制系统中传感器故障及其检测与诊断.暖通空调,2004,34(2):83-88
[58]陈友明.自动故障检测与诊断在暖通空调中的研究与应用.暖通空调,2004,34(3):29-33
[59]Hyvarinen J,Karki S.Building Optimization and Fault Diagnosis Source Book.Finland:IEA Annex 25,1996,25-26
[60]Arthur.D,Jouko Pakanen.Annex 34 computer Aided Evaluation of HVAC System Performance.In:Proceedings of VTT Symposium,London,2001,217-229
[61]X.M.Li,V.N.Hossein,J.C.Visier.Development of a fault diagnosis method for heating system using neural networks.ASHRAE Technical Data Bulletin,1996,12(2):48-55
[62]J.B.Gomm.Adaptive neural network approach to online learning for process fault diagnosis.Transaction of the Institute of measure and control,1998,20(3): 144-152
[63] Won-Yong Lee, John M.house, Nam-ho Kyong. Subsystem level fault diagnosis of a buildings air-handling unit using general regression neural networks. Applied energy, 2004, 27(2): 153-170
[64] H.Bechtler, M.W.Broune, P.K.Bansal. et al. New approach to dynamic modeling of vapor-compression liquid chillers: artificial neural networks. Applied thermal engineering, 2001, 21(9): 941-953
[65] D. Swider, M.W. Browne, P.K. Bansal. et al. Modeling of vapour-compression liquid chillers with neural networks. Applied Thermal Engineering, 2001, 21(3): 311-329
[66] R.P Leger, Wn. J. Garland, W.F.S Poehlman. Fault detection and diagnosis using statistical control charts and artificial neural networks. Aritificials intelligence in engineering, 1998, 12(1): 35-47
[67] R. Rengaswamy, D. Mylaraswamy, K. E. Arzen et al. A comparison of model-based and neural network-based diagnostic methods. Engineering application of artificial intelligence, 2001, 14(6): 805-818
[68] Haberl J.S, Claridge D.E. An expert system for building energy consumption analysis prototype results. ASHRAE Trans, 1987, 93(1): 445-467
[69] G. Xie. A tree-based expert system for fault tress construction. Reliability engineering and system safety, 1993, 40(3): 295-309.
[70] Stallard L. A. Model based expert system of failure detection and identification household refrigerators: [Master's thesis]. Indiana: School of mechanical engineering, Purdue University, 1989, 13-54
[71] A.L. Dexter, D. Ngo. Fault diagnosis in air conditioning systems: A multi-step fuzzy model-based approach. International Journal of HVAC&R Research, 2001, 7(1): 83-100
[72] W. huang, H.N. Lam. Using genetic algorithms to optimize controller parameters for HVAC system. Energy and Buildings, 1997, 26(3):277-282
[73] Huang S H., R.M.Nelson. Rule development and adjustment strategy of a fuzzy logic controller for an HVAC system. ASHRAE Transactions, 1994, 100(1): 841-850
[74] M.W.Browne, P.K.Bansal. An elemental NTU- ε model for vapour-compression liquid chillers. Int. J. of Refrigeration, 2001, 24(7): 612-627
[75] Browne M W, Bansal P K. Challenges in modeling vapor-compression liquid chillers. ASHRAE Transactions, 1998, 104(1): 474-486
[76] K.C.Ng, H.T.Chua, W. Ong. Diagnostics and optimization of reciprocating chillers: Theory and experiment. Applied thermal engineering, 1997, 17(3): 263-276
[77] Sung-Hwan Cho, Hoon-Cheol Yang, M. Zaheer-uddin et al. Transient pattern analysis for detection and diagnosis of HVAC systems. Energy conversion and mangement, 2005, 46(18): 3103-3116
[78] M.W.Browne, P.K.Bansal. Steady-state model of centrifugal liquid chillers. Int. J. of Refrigeration, 1998, 21(5): 343-358
[79] Gordon J M, Ng K C. Predictive and diagnostic aspects of a universal thermodynamic model for chillers. Int. J. Heat and Mass Transfer, 1995, 38(5): 807-818
[80] James Braun. Automated fault detection and diagnostics of rooftop air conditioner for California: [dissertation]. Indiana: Purdue University. 2003, 55-124
[81] H.T.Chua, K.g.Gg, J.M.Gordon. Experimental study of the fundamental properties of reciprocating chillers and their relation to thermodynamic modeling and chiller design. Int.J.of Heat Mass Transfer, 1996, 39(11): 2195-2204
[82] J.M.Gordon, Kim Choon Ng, Hui Tong Chua. Centrifugal chillers: thermodynamic modeling and a diagnostic case study. Int.J of Refrigerant, 1995, 18(4): 253-257
[83] Rhett David Gravies. Thermodynamic modeling and optimization of a screw compressor chiller and cooling tower system:[Master's thesis]. Texas: Texas A&M University, 2003, 25-56
[84] Derk.J.Swider. A comparison of empirically based steady-state models for vapor-compression liquid chillers. Applied thermal engineering, 2003, 23(5): 539-556
[85] Zhenggan Han, Weihua Li, Sirish L.Shah. Fault detection and isolation in the presence of process uncertainties. Control engineering practice, 2005, 13(5): 587-599
[86] Yongzhong Jia , T. Agami Reddy. Characteristic Physical Parameter Approach to Modeling Chillers Suitable for Fault Detection, Diagnosis, and Evaluation. Journal of Solar Energy Engineering, 2003, 125(3): 258-265
[87] Jingtan Cui, Shengwei Wang. A model-based online fault detection and diagnosis strategy for centrifugal chiller systems. International Journal of Thermal Sciences,2005,44(10):986-999
[88]Breuker.M.,T.Rossi,J.Braun.Smart maintence for rooftop units.ASHRAE Journal,2000,42(11):41-47
[89]R.Isermann.Process fault detection based on modeling and estimalion—a survey.Automatica,1984,20(4):387-404
[90]Paul M.Frank.Fault Diagnosis in Dynamic Systems Via State Estimation—A Survey.In:Proceedings of 1th European Workshop on Fault Diagnostics,Reliability Knowledge Based Approaches.Holland,1987,35-98
[91]Stylianou Meli.Application of classification functions to chiller fault detection and diagnosis.ASHRAE Trans.1997,103(1):645-656
[92]James E.Braun.Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment.Journal of Solar Energy Engineering,2003,125(3):266-274
[93]International Energy Agency(IEA).Demonstrating automation fault detection and diagnosis methods in real buildings.FINLAND:Technology Research Center of FINLAND,2001,47-51
[94]姜益强.空气源热泵冷热水机组故障分析与诊断建模:[哈尔滨工业大学博士论文].哈尔滨:哈尔滨工业大学,2002,35-61
[95]Krafthefer B C,Rask D R,Bonne U.Air conditioning and heat pump operation cost savings by maintaining coil cleanliness.ASHRAE Transactions,1987,93(1):1458-1473
[96]徐蔼婷.德尔菲法的应用及其难点.中国统计,2006,54(9):57-59
[97]王社伟,容错导航系统的可靠性分析:[北京航空航天大学博士论文].北京:北京航空航天大学,1999,25-47
[98]Chetouani Y.Fault detection by using the innovation signal:application to an exothermic reaction.Chemical engineering and processing,2004,43(12):1579-1585
[99]Kesunovic M,Rikalo I,Sobajic D.High-speed fault detection and classification with neural nets.Electric Power System Research,1995,34(2):109-116
[100]Berton A.,Hodouin D.Linear and bilinear fault detection and diagnosis based on mass and energy balance equations.Control Engineering Practice,2003,11(1):103-113
[101]J.Chen.,Howell J.A self-validating control system based approach to plant fault detection and diagnosis.Computers and Chemical Engineering,2001,25(2):337-358
[102]Vora N.;Tambe S.S.;Kulkarni B.D.Counterpagation neural networks for fault detection and diagnosis.Computers and Chemical Engineering,1997,21(2):177-185
[103]R.Petersen I.,C.McFarlane D.A methodology for robust fault detection in dynamic systems.Control Engineering Practice,2004,12(2):123-138
[104]Haberl J.S,Claridge D.E.An expert system for building energy consumption analysis prototype results.ASHRAE Trans,1987,93(1):445-467
[105]Dexter,A.L.,Benouarets M.Model-based fault diagnosis using fuzzy matching.IEEE Transactions,1997,27(5):673-682
[106]Srinivas Katipamula,Michael R Brambley.Methods for Fault Detection,Diagnostics,and Prognostics for Building Systems—A Review,Part Ⅱ.HVAC&R RESEARCH,2005,11(2):169-187
[107]Tsutsui H.,K.Kamimura.Chiller Condition Monitoring Using Topological Case-Based Modeling.ASHRAE Transactions,1996,102(1):641-648
[108]Tsutsui H.;Kurosaki A.,Sato T.et al.Fault detection using topological case based modeling and its application to chiller performance deterioration.In:Proceedings of10th Anniversary.Advanced Technologies in I & M.Hamamatsu,Japan,1994,390-393
[109]张国强,周天泰,林章等.基于ANN吸收式冷水机组模型的遗传算法最优控制.建筑热能通风空调,2005,24(6):1-5
[110]T.T.Chow,G.Q.Zhang,Z.Lin.applying neural network and genetic algorithm in chiller system optimization.In:Proceedings of Seventh International IBPSA Conference.Rio de Janeiro,Brazil,2001,13-15
[111]Chow T.T.;Zhang G.Q.;Lin Z et al.Global optimization of absorption chiller system by genetic algorithm and neural network.Energy and Buildings,2002,34(1):103-109
[112]Yung-chag Chang,Jui-kun Lin and Meng-Hsuan Chuang.Optimal chiller loading by genetic algorithm for reducing energy consumption.Energy and buildings,2005,37(2):147-155
[113]Lee W.Y.,C.Park,G.E.Kelly.Fault Detection in an Air-Handling Unit Using Residual and Recursive Parameter Identification Methods.ASHRAE Transactions,1996,102(1):528-539
[114]Lee W.Y.,J.M.House,D.R.Shin.Fault Diagnosis and Temperature Sensor Recovery for an Air-Handling Unit.ASHRAE Transact ions,1997,103(1):621-633
[115]Gaetan Kerschen,Pascal De Boe,Jean-Claude Golinval.Sensor validation using principal component analysis.Smart Mater.Struct,2005,14(1):1436-1442
[116]Shengwei Wang.Robust sensor fault diagnosis and validation in HVAC systems.Transactions of the Institute of Measurement and Control,2002,24(3):231-262
[117]Shengwei Wang,Youming Chen.Sensor validation and reconstruction for building central chilling systems based on principal component analysis.Energy Conversion and Management,2004,45(5):673-695
[118]Shengwei Wang,Jingtan Cui.Sensor-fault detection,diagnosis and estimation for centrifugal chiller systems using principal-component analysis method.Applied Energy,2005,82(3):197-213
[119]Mcadams.W.H.Heat Transmission(3~(rd)Edition).New-York:The Mc-Graw-Hill Press,1954,25-89
[120]连之伟.热质交换原理与设备(第1版).北京:中国建筑工业出版社,2001,50-61
[121]Jia Y.Model-based generic approaches for automated fault detection,diagnosis,evaluation(FDDE)and for accurate control of field operated centrifugal chillers:[doctorial dissertation].Philadelphia:Drexel University,2002,8-22
[122]M.W.Browne,P.K.Bansal.Transient simulation of a vapor-compression package liquid chillers.Int.J.of Refrigeration,2002,25(5):597-610
[123]H.T.Chua,K.C.Ng,W.Wang et al.Transient modeling of a two-bed silica gel-water adsorption chiller.International Journal of Heat and Mass Transfe,2004,47(4):659-669
[124]Fu Long,Ding Guoliang,Su Zujian,et al.Steady-state simulation of screw liquid chillers.Applied thermal engineering,2002,22(15):1731-1748
[125]Priya Sreedharan,Phil Haves.Comparison of Chiller Models for Use in model-Based Fault Detection.In:The Proceeding of International Conference for Enhanced Building Operations,Austin,TX,USA,2001,1-10
[126]胡锡伟,李君.VJ~(++)环境下的数据库访问技术探析.重庆大学学报(自然科学版).2004,27(S1):70-72
[127]李茜,李晓红,秦萍.EXCEL在CFD建模中的应用.建筑热能通风空调,2006,25(3):100-103
[128]Zhao Lei,M.Zaheeruddin.Dynamic simulation and analysis of a water chiller refrigeration system.Applied Thermal Engineering,2005,25(14):2258-2271
[129]连之伟.热质交换原理与设备(第2版).北京:中国建筑工业出版社,2006, 258-261
[130]杜清玲,李美芳,刘家科.机械设备预防维修周期的确定.机床与液压,2000,24(6):92-93
[131]蔡鹏举,史剑.广州“十一五”三大机制五大举措全面推进绿色建筑.广州建设.http://www.gzcc.gov.cn,2006-04-30
[1]亚热带地区建筑能源效率与节能.广东工业大学学报,2006,23(1):1-8
[3]暖通空调系统故障检测与诊断研究进展.暖通空调,2005,35(12):31-38
[1]陆亚俊,马最良,邹平华.暖通空调.北京:中国建筑工业出版社,2002,6-8
[2]马增林.中国制冷机组和大型空调设备的发展趋势.http://www.gzrd.com,2005-12-08
[3]龙惟定,白玮.我国民用建筑空调的发展前景.http://www.hvacinf.com,2005-11-10
[4]齐智,王勋陵.激光对蚕豆幼苗紫外线—B辐射损伤的防护作用.中国激光,2002,29(1):91-94
[5]Yingxin Zhu.Sustainable housing and urban construction in China.Energy and Buildings,2004,36(12):1289-1297
[6]Paul Crompton,Yanrui Wu.Energy consumption in China:past trends and future directions.Energy Economics,(2005),27(1):195-208
[7]John phelan,Michael Brandmuehl,Moncf Krart.Review of laboratory and field methods to measure fan,pump and chiller performance.ASHRAE Trans,1997,103(2),914-925
[8]House JM,Lee WY,Shin DR.Classification techniques for fault detection and diagnosis of air-hanlding unit.ASHRAE Transactions,1999,105(1):1087-1097
[9]Arthur.D,Jouko Pakanen.Annex 34 computer Aided Evaluation of HVAC System Performance.In:Proceedings of VTT Symposium.Finland,2001,13-19
[10]Todd M Rossi,James E Braun.Minimizing operating costs of vapor compression equipment with optimal service scheduling.Int.J of heating,ventilation,air-conditioning and refrigerating research,1996,2(1):3-26
[11]Katipamula S,Braubley M.Automated diagnostics improving building system and equipment performance.Energy user news,1998,23(4):1454-1462
[12]Yoshida H,T Iwami,H Yuzawa,et al.Typical faults of air-conditioning system and fault detection by ARX model and extended Kalman filter.ASHRAE Transactions,1996,102(1):557-564
[13]J.A.Giles,W.Ishida,world market for air-conditioning update 2001.http://www.bisra.co.uk.2005-06-04
[14]The Australian Greenhouse Office,commercial building air conditioning chillers.http://www.energyrating.gov.au.2004-04-06
[15]BSRIA Worldwide Market Intelligence.World Market for Air Conditioning 2002-entire study.London:BSRIA Limited,2003,1-46
[16] The Australian Greenhouse Office, commercial building air conditioning chillers. http://www.energyrating.gov.au/library/pubs/200415-mepschillers.pdf. 2004-04-06
[17] Ghiaus C. Fault diagnosis of air conditioning systems based on qualitative bond graph. Energy and Buildings, 1999,30(3): 221-232
[18] Haves P, T Salsbury, J A Wright. Condition monitoring in HVAC subsystems using first principles. ASHRAE Transactions, 1996, 102(1): 519-527
[19] Comstock M C, J E Braun, E A Groll. A survey of common faults in chillers. ASHRAE Transactions, 2002, 108(1): 819-825
[20] Hydeman M, N Webb, P Sreedharan et al. Development and testing of a reformulated regression-based electric chiller model. ASHRAE Transactions, 2002, 108(2): 1118-1127
[21] Michele B. Detecting changes in signals and systems-A survey. Automatic, 1988, 24(3): 309-326
[22] Frank P. M. Fault diagnosis systems using analytical and knowledge-based redundancy-A survey and some new results. Automation, 1990, 26(3): 459-474
[23] Janes. J. Gertler. Survey of model-based failure detection and isolation in complex plants. IEEE control system magazine, 1988, 8(6): 3-11
[24] Rolf. Isermann. Process fault detection based on modeling estimation methods-A survey. Automatica, 1988, 20(4): 384-407
[25] Alans. Willsky. A survey of design methods for failure detection in dynamic systems. Automatica, 1976, 12 (6): 601-611
[26] Rossi T. M., J. E. Braun. A Statistical rule-based fault detection and diagnostic method for vapor compression air conditioner. Int. J. of HVAC&R Research, 1997,3(1): 19-37
[27] Stylianou M, D Nikanpour. Performance monitoring, fault detection, and diagnosis of reciprocating chillers. ASHRAE Transactions, 1996, 102(1): 615-627.
[28] Breuker M. S, J. E. Braun. Common faults and their impacts for rooftop air conditioners. Int. J. of HVAC&R Research, 1998, 4(3): 303-308
[29] D. Bultman, L. Burmeister, V. Bortone et al. Vapor-compression refrigerator performance degradation due to condenser air flow blockage. America Society of Mechanical Engineers, 1993,93(4): 1-13
[30] Breuker M.S, J.E.Braun. Common faults and their impacts for rooftop air conditioners. Int. J. of HVAC&Research, 1998, 4(3):303-308
[31]Srinivas Katipamula,Michael R.Brambley.Methods for Fault Detection,Diagnostics,and Prognostics for Building Systems—A Review,Part Ⅰ.HVAC&R RESEARCH,2005,11(1):1-24
[32]Breuker M.S.,J.E.Braun.Evaluating the performance of a fault detection and diagnostics system for vapor compression equipment.Int.J.of HVAC&R Research,1998,4(4):401-425
[33]Stouppe D.E,Y.S.Lau.Air conditioning and refrigeration equipment failures.National Engineer,1989,93(9):14-17.
[34]Mekellar M.G.Fault diagnosis for a household refrigerator:[Master thesis].School of mechanical engineering.Indiana:Purdue University,1987,25-41
[35]Stallard L A.Model based expert system of failure detection and identification household refrigerators:[Master's thesis].Indiana:School of mechanical engineering,Purdue University,1989,28-75
[36]Yoshimura M,N.Ito.Effective diagnosis methods for air-conditioning equipment in communication buildings.In:Proceeding of IEEE INTELEC 89.Firenze,1989,1-17
[37]Kumamaru T,Utsunomiya T,Yamada Y.A fault diagnosis system for district heating and cooling facilities.In:Proceeding of International Electronics,Control and Instrumentation,Japan,1991,131-136
[38]Peitsman,H and V.Bakker.Application of Black-Box models to HVAC systems for fault detection.ASHRAE Transactions,1996,103(2):914-925
[39]Bailey M B.The design and viability of a probabilistic fault detection and diagnosis method for vapor compression cycle equipment:[PH.D thesis].Colorado:School of civil engineering,University of Colorado,1998,11-34
[40]Wagner J,Shoureshi.Fault detection diagnosis for thermofluid systems.Journal of dynamic systems,measurement and control,1992,114(4):699-706
[41]H.T.Grimmelius,J.Klein Woud,G Been.On-line failure diagnosis for compression refrigeration plants.Int.J.of Refrigeration,1995,18(1):31-41
[42]蔡立群.制冷系统故障诊断的专家系统:[上海交通大学硕士论文].上海:上海交通大学机械系,1993,24-45
[43]赵鹏.模糊专家系统在制冷系统在线故障诊断中的实现应用:[上海交通大学硕士论文].上海:上海交通大学机械系,1995,37-51
[44]鲍士雄,赵鹏.制冷系统故障诊断中模糊模式识别技术的应用.制冷学报,1998.20(2):20-27
[45]晋欣桥.冷水机组系统的温度传感器故障诊断.上海交通大学学报,2004, 38(1):976-981
[46]姜大勇,黄道.空气处理单元的故障检测与诊断方法.建筑热能通风空调,1999,18(2):106-108
[47]赵黎丽.两种人工智能方法应用于地热热泵系统辨识.系统仿真学报,2004,16(7):1376-1379
[48]李玉云.人工神经网络在暖通空调领域的应用研究发展.暖通空调,2001,31(1):77-79
[49]张振军.中央空调系统现场检测方法初探.制冷与空调,2003,3(4):24-26
[50]胡昶.基于温度检测和神经网络的空调负荷预测.仪器仪表学报,2003,24(1):427-428
[51]何书森.热泵干燥过程PID加Fuzzy调温的应用与实验.农业机械学报,1999,43(1):25-29
[52]刘宪英.神经网络法负荷预测及在蓄冰空调系统的优化运行.重庆建筑大学学报,1999,43(6):86-90
[53]刘贤坤.基于人工神经网络的变风量HVAC系统热力过程优化.四川制冷与空调,2003,19(2):1-5
[54]李韵.YCAM-H-600热泵故障分析及其对策.暖通空调,2003,33(2):102-104
[55]姜益强,姚扬,马最良.基于人工神经网络的空气源热泵冷热水机组的性能模拟.流体机械,2002,30(5):59-61
[56]姜益强,姚扬,马最良.空气源热泵冷水机组的故障诊断.制冷学报,2002,23(3):58-61
[57]陈友明,郝小礼.建筑能源管理与控制系统中传感器故障及其检测与诊断.暖通空调,2004,34(2):83-88
[58]陈友明.自动故障检测与诊断在暖通空调中的研究与应用.暖通空调,2004,34(3):29-33
[59]Hyvarinen J,Karki S.Building Optimization and Fault Diagnosis Source Book.Finland:IEA Annex 25,1996,25-26
[60]Arthur.D,Jouko Pakanen.Annex 34 computer Aided Evaluation of HVAC System Performance.In:Proceedings of VTT Symposium,London,2001,217-229
[61]X.M.Li,V.N.Hossein,J.C.Visier.Development of a fault diagnosis method for heating system using neural networks.ASHRAE Technical Data Bulletin,1996,12(2):48-55
[62]J.B.Gomm.Adaptive neural network approach to online learning for process fault diagnosis.Transaction of the Institute of measure and control,1998,20(3): 144-152
[63] Won-Yong Lee, John M.house, Nam-ho Kyong. Subsystem level fault diagnosis of a buildings air-handling unit using general regression neural networks. Applied energy, 2004, 27(2): 153-170
[64] H.Bechtler, M.W.Broune, P.K.Bansal. et al. New approach to dynamic modeling of vapor-compression liquid chillers: artificial neural networks. Applied thermal engineering, 2001, 21(9): 941-953
[65] D. Swider, M.W. Browne, P.K. Bansal. et al. Modeling of vapour-compression liquid chillers with neural networks. Applied Thermal Engineering, 2001, 21(3): 311-329
[66] R.P Leger, Wn. J. Garland, W.F.S Poehlman. Fault detection and diagnosis using statistical control charts and artificial neural networks. Aritificials intelligence in engineering, 1998, 12(1): 35-47
[67] R. Rengaswamy, D. Mylaraswamy, K. E. Arzen et al. A comparison of model-based and neural network-based diagnostic methods. Engineering application of artificial intelligence, 2001, 14(6): 805-818
[68] Haberl J.S, Claridge D.E. An expert system for building energy consumption analysis prototype results. ASHRAE Trans, 1987, 93(1): 445-467
[69] G. Xie. A tree-based expert system for fault tress construction. Reliability engineering and system safety, 1993, 40(3): 295-309.
[70] Stallard L. A. Model based expert system of failure detection and identification household refrigerators: [Master's thesis]. Indiana: School of mechanical engineering, Purdue University, 1989, 13-54
[71] A.L. Dexter, D. Ngo. Fault diagnosis in air conditioning systems: A multi-step fuzzy model-based approach. International Journal of HVAC&R Research, 2001, 7(1): 83-100
[72] W. huang, H.N. Lam. Using genetic algorithms to optimize controller parameters for HVAC system. Energy and Buildings, 1997, 26(3):277-282
[73] Huang S H., R.M.Nelson. Rule development and adjustment strategy of a fuzzy logic controller for an HVAC system. ASHRAE Transactions, 1994, 100(1): 841-850
[74] M.W.Browne, P.K.Bansal. An elemental NTU- ε model for vapour-compression liquid chillers. Int. J. of Refrigeration, 2001, 24(7): 612-627
[75] Browne M W, Bansal P K. Challenges in modeling vapor-compression liquid chillers. ASHRAE Transactions, 1998, 104(1): 474-486
[76] K.C.Ng, H.T.Chua, W. Ong. Diagnostics and optimization of reciprocating chillers: Theory and experiment. Applied thermal engineering, 1997, 17(3): 263-276
[77] Sung-Hwan Cho, Hoon-Cheol Yang, M. Zaheer-uddin et al. Transient pattern analysis for detection and diagnosis of HVAC systems. Energy conversion and mangement, 2005, 46(18): 3103-3116
[78] M.W.Browne, P.K.Bansal. Steady-state model of centrifugal liquid chillers. Int. J. of Refrigeration, 1998, 21(5): 343-358
[79] Gordon J M, Ng K C. Predictive and diagnostic aspects of a universal thermodynamic model for chillers. Int. J. Heat and Mass Transfer, 1995, 38(5): 807-818
[80] James Braun. Automated fault detection and diagnostics of rooftop air conditioner for California: [dissertation]. Indiana: Purdue University. 2003, 55-124
[81] H.T.Chua, K.g.Gg, J.M.Gordon. Experimental study of the fundamental properties of reciprocating chillers and their relation to thermodynamic modeling and chiller design. Int.J.of Heat Mass Transfer, 1996, 39(11): 2195-2204
[82] J.M.Gordon, Kim Choon Ng, Hui Tong Chua. Centrifugal chillers: thermodynamic modeling and a diagnostic case study. Int.J of Refrigerant, 1995, 18(4): 253-257
[83] Rhett David Gravies. Thermodynamic modeling and optimization of a screw compressor chiller and cooling tower system:[Master's thesis]. Texas: Texas A&M University, 2003, 25-56
[84] Derk.J.Swider. A comparison of empirically based steady-state models for vapor-compression liquid chillers. Applied thermal engineering, 2003, 23(5): 539-556
[85] Zhenggan Han, Weihua Li, Sirish L.Shah. Fault detection and isolation in the presence of process uncertainties. Control engineering practice, 2005, 13(5): 587-599
[86] Yongzhong Jia , T. Agami Reddy. Characteristic Physical Parameter Approach to Modeling Chillers Suitable for Fault Detection, Diagnosis, and Evaluation. Journal of Solar Energy Engineering, 2003, 125(3): 258-265
[87] Jingtan Cui, Shengwei Wang. A model-based online fault detection and diagnosis strategy for centrifugal chiller systems. International Journal of Thermal Sciences,2005,44(10):986-999
[88]Breuker.M.,T.Rossi,J.Braun.Smart maintence for rooftop units.ASHRAE Journal,2000,42(11):41-47
[89]R.Isermann.Process fault detection based on modeling and estimalion—a survey.Automatica,1984,20(4):387-404
[90]Paul M.Frank.Fault Diagnosis in Dynamic Systems Via State Estimation—A Survey.In:Proceedings of 1th European Workshop on Fault Diagnostics,Reliability Knowledge Based Approaches.Holland,1987,35-98
[91]Stylianou Meli.Application of classification functions to chiller fault detection and diagnosis.ASHRAE Trans.1997,103(1):645-656
[92]James E.Braun.Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment.Journal of Solar Energy Engineering,2003,125(3):266-274
[93]International Energy Agency(IEA).Demonstrating automation fault detection and diagnosis methods in real buildings.FINLAND:Technology Research Center of FINLAND,2001,47-51
[94]姜益强.空气源热泵冷热水机组故障分析与诊断建模:[哈尔滨工业大学博士论文].哈尔滨:哈尔滨工业大学,2002,35-61
[95]Krafthefer B C,Rask D R,Bonne U.Air conditioning and heat pump operation cost savings by maintaining coil cleanliness.ASHRAE Transactions,1987,93(1):1458-1473
[96]徐蔼婷.德尔菲法的应用及其难点.中国统计,2006,54(9):57-59
[97]王社伟,容错导航系统的可靠性分析:[北京航空航天大学博士论文].北京:北京航空航天大学,1999,25-47
[98]Chetouani Y.Fault detection by using the innovation signal:application to an exothermic reaction.Chemical engineering and processing,2004,43(12):1579-1585
[99]Kesunovic M,Rikalo I,Sobajic D.High-speed fault detection and classification with neural nets.Electric Power System Research,1995,34(2):109-116
[100]Berton A.,Hodouin D.Linear and bilinear fault detection and diagnosis based on mass and energy balance equations.Control Engineering Practice,2003,11(1):103-113
[101]J.Chen.,Howell J.A self-validating control system based approach to plant fault detection and diagnosis.Computers and Chemical Engineering,2001,25(2):337-358
[102]Vora N.;Tambe S.S.;Kulkarni B.D.Counterpagation neural networks for fault detection and diagnosis.Computers and Chemical Engineering,1997,21(2):177-185
[103]R.Petersen I.,C.McFarlane D.A methodology for robust fault detection in dynamic systems.Control Engineering Practice,2004,12(2):123-138
[104]Haberl J.S,Claridge D.E.An expert system for building energy consumption analysis prototype results.ASHRAE Trans,1987,93(1):445-467
[105]Dexter,A.L.,Benouarets M.Model-based fault diagnosis using fuzzy matching.IEEE Transactions,1997,27(5):673-682
[106]Srinivas Katipamula,Michael R Brambley.Methods for Fault Detection,Diagnostics,and Prognostics for Building Systems—A Review,Part Ⅱ.HVAC&R RESEARCH,2005,11(2):169-187
[107]Tsutsui H.,K.Kamimura.Chiller Condition Monitoring Using Topological Case-Based Modeling.ASHRAE Transactions,1996,102(1):641-648
[108]Tsutsui H.;Kurosaki A.,Sato T.et al.Fault detection using topological case based modeling and its application to chiller performance deterioration.In:Proceedings of10th Anniversary.Advanced Technologies in I & M.Hamamatsu,Japan,1994,390-393
[109]张国强,周天泰,林章等.基于ANN吸收式冷水机组模型的遗传算法最优控制.建筑热能通风空调,2005,24(6):1-5
[110]T.T.Chow,G.Q.Zhang,Z.Lin.applying neural network and genetic algorithm in chiller system optimization.In:Proceedings of Seventh International IBPSA Conference.Rio de Janeiro,Brazil,2001,13-15
[111]Chow T.T.;Zhang G.Q.;Lin Z et al.Global optimization of absorption chiller system by genetic algorithm and neural network.Energy and Buildings,2002,34(1):103-109
[112]Yung-chag Chang,Jui-kun Lin and Meng-Hsuan Chuang.Optimal chiller loading by genetic algorithm for reducing energy consumption.Energy and buildings,2005,37(2):147-155
[113]Lee W.Y.,C.Park,G.E.Kelly.Fault Detection in an Air-Handling Unit Using Residual and Recursive Parameter Identification Methods.ASHRAE Transactions,1996,102(1):528-539
[114]Lee W.Y.,J.M.House,D.R.Shin.Fault Diagnosis and Temperature Sensor Recovery for an Air-Handling Unit.ASHRAE Transact ions,1997,103(1):621-633
[115]Gaetan Kerschen,Pascal De Boe,Jean-Claude Golinval.Sensor validation using principal component analysis.Smart Mater.Struct,2005,14(1):1436-1442
[116]Shengwei Wang.Robust sensor fault diagnosis and validation in HVAC systems.Transactions of the Institute of Measurement and Control,2002,24(3):231-262
[117]Shengwei Wang,Youming Chen.Sensor validation and reconstruction for building central chilling systems based on principal component analysis.Energy Conversion and Management,2004,45(5):673-695
[118]Shengwei Wang,Jingtan Cui.Sensor-fault detection,diagnosis and estimation for centrifugal chiller systems using principal-component analysis method.Applied Energy,2005,82(3):197-213
[119]Mcadams.W.H.Heat Transmission(3~(rd)Edition).New-York:The Mc-Graw-Hill Press,1954,25-89
[120]连之伟.热质交换原理与设备(第1版).北京:中国建筑工业出版社,2001,50-61
[121]Jia Y.Model-based generic approaches for automated fault detection,diagnosis,evaluation(FDDE)and for accurate control of field operated centrifugal chillers:[doctorial dissertation].Philadelphia:Drexel University,2002,8-22
[122]M.W.Browne,P.K.Bansal.Transient simulation of a vapor-compression package liquid chillers.Int.J.of Refrigeration,2002,25(5):597-610
[123]H.T.Chua,K.C.Ng,W.Wang et al.Transient modeling of a two-bed silica gel-water adsorption chiller.International Journal of Heat and Mass Transfe,2004,47(4):659-669
[124]Fu Long,Ding Guoliang,Su Zujian,et al.Steady-state simulation of screw liquid chillers.Applied thermal engineering,2002,22(15):1731-1748
[125]Priya Sreedharan,Phil Haves.Comparison of Chiller Models for Use in model-Based Fault Detection.In:The Proceeding of International Conference for Enhanced Building Operations,Austin,TX,USA,2001,1-10
[126]胡锡伟,李君.VJ~(++)环境下的数据库访问技术探析.重庆大学学报(自然科学版).2004,27(S1):70-72
[127]李茜,李晓红,秦萍.EXCEL在CFD建模中的应用.建筑热能通风空调,2006,25(3):100-103
[128]Zhao Lei,M.Zaheeruddin.Dynamic simulation and analysis of a water chiller refrigeration system.Applied Thermal Engineering,2005,25(14):2258-2271
[129]连之伟.热质交换原理与设备(第2版).北京:中国建筑工业出版社,2006, 258-261
[130]杜清玲,李美芳,刘家科.机械设备预防维修周期的确定.机床与液压,2000,24(6):92-93
[131]蔡鹏举,史剑.广州“十一五”三大机制五大举措全面推进绿色建筑.广州建设.http://www.gzcc.gov.cn,2006-04-30