单、双级耦合热泵系统故障分析与诊断研究
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摘要
能源和环保问题是当今世界共同关注的热点,也是今后长期存在的问题。我国是能源生产和消费大国,面对新世纪,如何保持能源、经济和环境的可持续发展是我们面对的一个重大战略问题。而我国北方目前常用的几种供暖方式,大都是“热源消耗高品位能源、向建筑物室内提供低温热源、向环境排放废物”的单向型模式,将给我国未来的能源供应和环境保护带来巨大压力。单、双级耦合热泵系统(Single-double stage coupling heat pump system,简称SDSCHPS)是一种以热泵理论为基础,以“生态循环供暖”为理念的新型热泵供暖系统,该系统是HVAC&R领域为推动热泵技术在北方应用而进行系统创新的一个成功案例,具有较高的实际推广价值。但是,由于SDSCHPS结构的复杂性、制热运行时环境的特殊性、运行模式的多样性和多种故障的渐变性等原因,使得系统运行的可靠性、制热效率和使用寿命受到一定影响。而目前HVAC&R领域的多数故障检测与诊断系统仅有当温度、压力等超过预设值时切断设备的功能,既难以对设备当前的运行状态进行监测,又无法提供故障产生的具体原因和解决故障的具体措施,导致了暖通空调设备的运行可靠性下降、维护与维修费用的增加和大量不必要的能源浪费。为提高SDSCHPS的运行可靠性、节能性和智能化管理水平,为其在我国北方地区的推广应用提供有力保障,本文对系统中可能出现的主要故障进行深入分析和实验研究,探求系统在低温环境下的运行规律及故障与其征兆之间的关系,并初步构建融合人工神经网络和专家系统的分布式故障诊断智能系统模型,从而为SDSCHPS的故障诊断系统的开发提供较为完善的研究基础和理论框架。
应用层次分解法,分别构建SDSCHPS中两大主要设备——空气/水热泵机组和水/水热泵机组的故障树,采用故障树分析法对SDSCHPS中可能出现的各种故障进行深入研究,按其出现部位,将其故障主要分为三大类,即制冷系统故障、除霜故障和控制系统故障。并采用故障出现频率和维修费用比例作为评价指标,对这些故障进行重要度分析,找出构建SDSCHPS故障诊断系统所需研究的重要故障和常见故障。
由于热泵机组在“非健康”状态下运行时性能会有所降低,本文提出一种基于广义回归神经网络(GRNN)的SDSCHPS状态监测方法,充分利用GRNN良好的预测能力,通过对系统运行时制热能效比EER预测值和实际值的比较分析,实现对SDSCHPS的状态监测,完成了设备故障检测与诊断的第一个任务。并通过实验测取不同室外环境和运行模式下SDSCHPS的性能参数,并以此作为样本,通过拟合训练和预测分析,验证所构建的GRNN模型应用于SDSCHPS状态监测的可行性和有效性。
分析了SDSCHPS中制冷系统的常见故障及其特征,确定对其进行故障诊断和推理所需要的特征参数和测点布置;并应用定性理论分析方法,对SDSCHPS制冷系统的常见软故障与特征参数之间的关系进行定性分析,并分别构建SDSCHPS中空气/水热泵机组和水/水热泵机组的制冷系统标准软故障模式库;然后分别构建SDSCHPS不同运行模式下制冷系统基于GRNN的故障诊断和基于规则的故障推理模型,实现了对SDSCHPS中制冷系统故障的实时诊断及推理。
通过实验研究空气源热泵机组在不同程度的室外换热器堵塞故障工况下,其主要运行特性参数和性能参数的动态变化规律,验证了前文通过定性理论分析所获取的典型故障之一,即空气源热泵机组室外换热器传热效果不佳软故障发生时,故障与征兆之间对应关系的准确性,并深入分析室外换热器堵塞故障对整个空气源热泵机组运行工况和性能的危害。通过实验研究了在正常除霜、提前除霜和滞后除霜等不同除霜工况下,空气源热泵机组主要运行参数和性能参数的动态变化规律,并深入分析除霜故障对机组的运行工况和性能的危害;同时,利用概率神经网络(PNN)良好的模式识别能力,构建一个简单的除霜故障诊断模型,实现了对空气源热泵机组除霜故障的实时检测和诊断;为减少除霜故障的发生,本文还提出一种改进的除霜控制方法,即基于室外风机电流和盘管温度的除霜控制方法,利用实验结果对该方法的可行性进行分析验证,并设计出该除霜控制方法的基本流程。
由于SDSCHPS控制系统中的多传感器测量值之间存在冗余关系,以信息融合理论为基础,以人工神经网络(ANN)为工具,提出一种多传感器故障诊断及容错方法,实现了对SDSCHPS中多传感器的故障诊断及信号恢复。并结合一组实验数据,分析验证该方法对SDSCHPS中常见传感器的四类故障的诊断及容错效果。
针对传统的专家系统和人工神经网络应用于故障诊断时各自存在的不足,并根据SDSCHPS的结构特点和运行模式,提出一种融合人工神经网络和专家系统的分布式故障诊断智能系统,既克服了专家系统和神经网络各自的缺点,有充分利用了二者的长处,解决了单独应用专家系统和神经网络所无法解决的问题。同时,深入研究SDSCHPS分布式诊断过程中诊断任务的分析与分解、各子任务的求解、各子任务解的综合等问题,初步构建SDSCHPS的分布式故障诊断智能系统框架,从而为SDSCHPS进行故障诊断系统开发及应用提供了理论基础。
本文的研究工作对推进SDSCHPS智能型机电一体化、延长设备使用寿命、提高系统的运行可靠性和效率、降低能耗、减少维护和维修费用等,有着十分重要的实际意义,而且为HVAC&R领域故障诊断智能系统的构建从方法上进行了新的探索,提供了一条切实可行的新途径。
The issue of energy sources and environment protection is always a focus in our current world, and will be a long-term problem existing in future. China is one of the countries which explode and consume great amount of energy sources every year, and in the 21 century, how to keep harmonious development is a strategic question which we have to face. At the present time, the common heating methods in north China mainly are single-direction modes, which will bring huge pressure on our future. Single double-stage coupling heat pump system (SDSCHPS) is a new kind of heating system which based on heat pump theory and zoology recycle heating concept, and also a successful case to extent air-source heat pump to the north. However, because of its complicated structure, peculiar operation environment, variety of operation modes and gradualness of many faults, the operation reliability, efficiency and life of SDSCHPS are influenced in a certain extent. Currently, most fault detection and diagnosis (FDD) systems in HVAC field only have the shut-down function when correlative temperature or pressure exceeds scheduled parameter. The existing FDD systems not only can’t monitor unit’s operation conditions, but also can’t give concrete reason and effective solution of fault. To improve its operation reliability, energy-economy and intellectualized manage ability, this dissertation theoretically analyzed and experimentally studied the mechanism of main faults which may occur in the SDSCHPS, then searched the logic relationship between the faults and symptoms, and construct its distributed FDD system which combines artificial neural network (ANN) and expert system (ES), thereby, brought integrated frame of FDD system in the SDSCHPS.
By applying hierarchy-decompose method, the fault trees of the air-to-water heat pump unit and the water-to-water heat pump unit, namely, the central equipment of the SDSCHPS were set up separately, and using occurring frequency and maintenance expense as evaluation index, the important faults and common faults of the SDSCHPS were found out. According to its occurring position, the faults of the SDSCHPS were classified to there kinds, namely, refrigeration system’s faults, defrost system’s faults and control system’s faults.
Owing to its performance would be lower when running in unhealthy condition, this dissertation put forward a state-monitor method of the SDSCHPS based on general regression neural network (GRNN). By utilizing the nice forecast ability of GRNN fully, the first role of the SDSCHPS’s FDD system was realized, which means that the operation state-monitor was completed by comparing and analyzed the forecasting EER and the real EER of the SDSCHPS. Then, the performance parameters of the SDSCHPS were measured under different operation conditions and modes by experiments, which acted as samples of GRNN model, and the feasibility and validity of the state-monitor method were validated by simulating and analyzing.
The common faults of refrigeration system in the SDSCHPS were investigated, and the characteristic parameters were confirmed, which were needed to fault diagnosis and reason. By applying qualitative analysis method, the relationship between the common faults and its symptoms were analyzed and the standard fault mode-base of the SDSCHPS were constructed. Finally, the FDD models based on GRNN and the fault reasoning based on rule of refrigeration system under different operation modes were set up, and the real-time FDD and reasoning of the refrigeration system were realized.
The relationship between one kind of common fault occurring in air-source heat pump (ASHP) and its symptom were validated experimentally. This fault will happen when the outdoor heat exchanger’s air-side of ASHP jams with ice, snow, dirt or some other stuff, which results the worse of the heat exchanging efficiency. By experiments, the dynamic variety rules of those main operation characteristic parameters were measured and analyzed, the relationship between the fault and its symptom was substantiated and different grade faults’harm on the unit’s operation conditions and performance was studied quantificationally.
Similarly, the dynamic variety rules of main operation characteristic parameters and performance parameters of an ASHP unit were investigated experimentally when it run under different defrost states, such as normal defrost state, advance defrost state and lag defrost state, at the same time, the defrost faults’harm on the unit’s operation and performance was also studied. Then, applying outstanding mode-identify ability of the probability neural network (PNN), a simple defrost FDD model was set up, which helped to detect and diagnose defrost fault of ASHP in time. Finally, to diminish the defrost faults, this dissertation brought out an improved defrost control method, which was based on the change of outdoor fan current and wing surface temperature. The feasibility of this method was analyzed and validated by experiments, and its basic flow was designed elaborately.
Because of the redundancy relationship between the multi-sensor measure data in the control system of the SDSCHPS, this dissertation put forward a kind of multi-sensor fault diagnosis and error-tolerance method, which was established on information amalgamation theory and ANN tool. Through simulating with a group of experiment data, the diagnosis and error-tolerance effect on four kinds of common faults, which were prone to occur in the control system of the SDSCHPS, was analyzed and validated thoroughly.
Based on the limitation of traditional ES or ANN, this dissertation pointed out a distributed FDD artificial system in the SDSCHPS, which used ANN and ES simultaneously. The distributed FDD system not only could overcome the limitation of ANN or ES, but also take full advantage of these two tools, thus solve the problem which couldn’t settled by only one of them. On the other hand, in the distributed FDD system, these problems, such as the decomposition of whole FDD task, solution of every subtask, integration of each subtask’s answer, and so on, were researched thoroughly, and the theoretic frame of the distributed FDD system in the SDSCHPS was established, therefore, the theory foundation to design and apply of the distributed FDD artificial system was realized.
The study work of this dissertation was very important for these problems’solution, such as advancing artificial machine-electrification incorporation, prolonging the life-span of HVAC equipment, enhancing the operation reliability and efficiency, reducing energy resources consumption, and diminishing maintenance expense, and so on. Therefore, some new methods about artificial FDD system’s construction were probed, and a new viable way was offered to settle FDD problem in HVAC field.
应用层次分解法,分别构建SDSCHPS中两大主要设备——空气/水热泵机组和水/水热泵机组的故障树,采用故障树分析法对SDSCHPS中可能出现的各种故障进行深入研究,按其出现部位,将其故障主要分为三大类,即制冷系统故障、除霜故障和控制系统故障。并采用故障出现频率和维修费用比例作为评价指标,对这些故障进行重要度分析,找出构建SDSCHPS故障诊断系统所需研究的重要故障和常见故障。
由于热泵机组在“非健康”状态下运行时性能会有所降低,本文提出一种基于广义回归神经网络(GRNN)的SDSCHPS状态监测方法,充分利用GRNN良好的预测能力,通过对系统运行时制热能效比EER预测值和实际值的比较分析,实现对SDSCHPS的状态监测,完成了设备故障检测与诊断的第一个任务。并通过实验测取不同室外环境和运行模式下SDSCHPS的性能参数,并以此作为样本,通过拟合训练和预测分析,验证所构建的GRNN模型应用于SDSCHPS状态监测的可行性和有效性。
分析了SDSCHPS中制冷系统的常见故障及其特征,确定对其进行故障诊断和推理所需要的特征参数和测点布置;并应用定性理论分析方法,对SDSCHPS制冷系统的常见软故障与特征参数之间的关系进行定性分析,并分别构建SDSCHPS中空气/水热泵机组和水/水热泵机组的制冷系统标准软故障模式库;然后分别构建SDSCHPS不同运行模式下制冷系统基于GRNN的故障诊断和基于规则的故障推理模型,实现了对SDSCHPS中制冷系统故障的实时诊断及推理。
通过实验研究空气源热泵机组在不同程度的室外换热器堵塞故障工况下,其主要运行特性参数和性能参数的动态变化规律,验证了前文通过定性理论分析所获取的典型故障之一,即空气源热泵机组室外换热器传热效果不佳软故障发生时,故障与征兆之间对应关系的准确性,并深入分析室外换热器堵塞故障对整个空气源热泵机组运行工况和性能的危害。通过实验研究了在正常除霜、提前除霜和滞后除霜等不同除霜工况下,空气源热泵机组主要运行参数和性能参数的动态变化规律,并深入分析除霜故障对机组的运行工况和性能的危害;同时,利用概率神经网络(PNN)良好的模式识别能力,构建一个简单的除霜故障诊断模型,实现了对空气源热泵机组除霜故障的实时检测和诊断;为减少除霜故障的发生,本文还提出一种改进的除霜控制方法,即基于室外风机电流和盘管温度的除霜控制方法,利用实验结果对该方法的可行性进行分析验证,并设计出该除霜控制方法的基本流程。
由于SDSCHPS控制系统中的多传感器测量值之间存在冗余关系,以信息融合理论为基础,以人工神经网络(ANN)为工具,提出一种多传感器故障诊断及容错方法,实现了对SDSCHPS中多传感器的故障诊断及信号恢复。并结合一组实验数据,分析验证该方法对SDSCHPS中常见传感器的四类故障的诊断及容错效果。
针对传统的专家系统和人工神经网络应用于故障诊断时各自存在的不足,并根据SDSCHPS的结构特点和运行模式,提出一种融合人工神经网络和专家系统的分布式故障诊断智能系统,既克服了专家系统和神经网络各自的缺点,有充分利用了二者的长处,解决了单独应用专家系统和神经网络所无法解决的问题。同时,深入研究SDSCHPS分布式诊断过程中诊断任务的分析与分解、各子任务的求解、各子任务解的综合等问题,初步构建SDSCHPS的分布式故障诊断智能系统框架,从而为SDSCHPS进行故障诊断系统开发及应用提供了理论基础。
本文的研究工作对推进SDSCHPS智能型机电一体化、延长设备使用寿命、提高系统的运行可靠性和效率、降低能耗、减少维护和维修费用等,有着十分重要的实际意义,而且为HVAC&R领域故障诊断智能系统的构建从方法上进行了新的探索,提供了一条切实可行的新途径。
The issue of energy sources and environment protection is always a focus in our current world, and will be a long-term problem existing in future. China is one of the countries which explode and consume great amount of energy sources every year, and in the 21 century, how to keep harmonious development is a strategic question which we have to face. At the present time, the common heating methods in north China mainly are single-direction modes, which will bring huge pressure on our future. Single double-stage coupling heat pump system (SDSCHPS) is a new kind of heating system which based on heat pump theory and zoology recycle heating concept, and also a successful case to extent air-source heat pump to the north. However, because of its complicated structure, peculiar operation environment, variety of operation modes and gradualness of many faults, the operation reliability, efficiency and life of SDSCHPS are influenced in a certain extent. Currently, most fault detection and diagnosis (FDD) systems in HVAC field only have the shut-down function when correlative temperature or pressure exceeds scheduled parameter. The existing FDD systems not only can’t monitor unit’s operation conditions, but also can’t give concrete reason and effective solution of fault. To improve its operation reliability, energy-economy and intellectualized manage ability, this dissertation theoretically analyzed and experimentally studied the mechanism of main faults which may occur in the SDSCHPS, then searched the logic relationship between the faults and symptoms, and construct its distributed FDD system which combines artificial neural network (ANN) and expert system (ES), thereby, brought integrated frame of FDD system in the SDSCHPS.
By applying hierarchy-decompose method, the fault trees of the air-to-water heat pump unit and the water-to-water heat pump unit, namely, the central equipment of the SDSCHPS were set up separately, and using occurring frequency and maintenance expense as evaluation index, the important faults and common faults of the SDSCHPS were found out. According to its occurring position, the faults of the SDSCHPS were classified to there kinds, namely, refrigeration system’s faults, defrost system’s faults and control system’s faults.
Owing to its performance would be lower when running in unhealthy condition, this dissertation put forward a state-monitor method of the SDSCHPS based on general regression neural network (GRNN). By utilizing the nice forecast ability of GRNN fully, the first role of the SDSCHPS’s FDD system was realized, which means that the operation state-monitor was completed by comparing and analyzed the forecasting EER and the real EER of the SDSCHPS. Then, the performance parameters of the SDSCHPS were measured under different operation conditions and modes by experiments, which acted as samples of GRNN model, and the feasibility and validity of the state-monitor method were validated by simulating and analyzing.
The common faults of refrigeration system in the SDSCHPS were investigated, and the characteristic parameters were confirmed, which were needed to fault diagnosis and reason. By applying qualitative analysis method, the relationship between the common faults and its symptoms were analyzed and the standard fault mode-base of the SDSCHPS were constructed. Finally, the FDD models based on GRNN and the fault reasoning based on rule of refrigeration system under different operation modes were set up, and the real-time FDD and reasoning of the refrigeration system were realized.
The relationship between one kind of common fault occurring in air-source heat pump (ASHP) and its symptom were validated experimentally. This fault will happen when the outdoor heat exchanger’s air-side of ASHP jams with ice, snow, dirt or some other stuff, which results the worse of the heat exchanging efficiency. By experiments, the dynamic variety rules of those main operation characteristic parameters were measured and analyzed, the relationship between the fault and its symptom was substantiated and different grade faults’harm on the unit’s operation conditions and performance was studied quantificationally.
Similarly, the dynamic variety rules of main operation characteristic parameters and performance parameters of an ASHP unit were investigated experimentally when it run under different defrost states, such as normal defrost state, advance defrost state and lag defrost state, at the same time, the defrost faults’harm on the unit’s operation and performance was also studied. Then, applying outstanding mode-identify ability of the probability neural network (PNN), a simple defrost FDD model was set up, which helped to detect and diagnose defrost fault of ASHP in time. Finally, to diminish the defrost faults, this dissertation brought out an improved defrost control method, which was based on the change of outdoor fan current and wing surface temperature. The feasibility of this method was analyzed and validated by experiments, and its basic flow was designed elaborately.
Because of the redundancy relationship between the multi-sensor measure data in the control system of the SDSCHPS, this dissertation put forward a kind of multi-sensor fault diagnosis and error-tolerance method, which was established on information amalgamation theory and ANN tool. Through simulating with a group of experiment data, the diagnosis and error-tolerance effect on four kinds of common faults, which were prone to occur in the control system of the SDSCHPS, was analyzed and validated thoroughly.
Based on the limitation of traditional ES or ANN, this dissertation pointed out a distributed FDD artificial system in the SDSCHPS, which used ANN and ES simultaneously. The distributed FDD system not only could overcome the limitation of ANN or ES, but also take full advantage of these two tools, thus solve the problem which couldn’t settled by only one of them. On the other hand, in the distributed FDD system, these problems, such as the decomposition of whole FDD task, solution of every subtask, integration of each subtask’s answer, and so on, were researched thoroughly, and the theoretic frame of the distributed FDD system in the SDSCHPS was established, therefore, the theory foundation to design and apply of the distributed FDD artificial system was realized.
The study work of this dissertation was very important for these problems’solution, such as advancing artificial machine-electrification incorporation, prolonging the life-span of HVAC equipment, enhancing the operation reliability and efficiency, reducing energy resources consumption, and diminishing maintenance expense, and so on. Therefore, some new methods about artificial FDD system’s construction were probed, and a new viable way was offered to settle FDD problem in HVAC field.
引文
1 中华人民共和国国家发展和改革委员会. 国民经济和社会发展第十个五年计划能源发展重点专项规划. http://dp.cei.gov.cn/lszl/nengyuan.htm
2 陈清泰. 中国能源战略走一步看一步不可取. 能源战略和改革国际研讨会. 北京,2003 年 11 月
3 http://www.cenews.com.cn/news/2005-08-19/48586.php
4 朗四维. 中国建筑环境设备发展动向. 中日建筑环境设备高级论坛论文集. 上海,2003:27~50
5 陈滨,孙鹏,丁颖慧等. 住宅能源消费结构及自然能源应用. 暖通空调. 2005,35(3):45~50
6 马最良. 替代寒冷地区传统供暖的新型热泵供暖方式的探讨. 暖通空调新技术 3. 2001:31~35
7 陆亚俊,马最良,姚扬. 空调工程中的制冷技术. 哈尔滨:哈尔滨工程大学出版社. 1997:217~229
8 喻银平,马最良. 双级耦合式热泵系统在寒冷地区应用的可行性分析. 电力需求测管理. 2002,4(2):39~42
9 龙惟定,白玮,张蓓红. 中国住宅空调的未来发展. 暖通空调. 2002,32(4):43~47
10 沈明,宋之平. 空气源热泵应用范围北扩的可能性分析及其技术措施研究. 暖通空调. 2002,32(6):37~39
11 马最良,杨自强,姚杨等.空气源热泵冷热水机组在寒冷地区应用的可行性分析.暖通空调. 2001,34(3):28~31
12 喻银平. 双级耦合热泵系统模拟研究. 哈尔滨工业大学工学硕士学位论文. 2002:15~40
13 连之伟,张欧. 风冷热泵在西安地区的运行效果测定及分析. 暖通空调. 1998,28(5):17~20
14 张积太. 空气源热泵冷热水机组在胶东地区的设计尝试及技术经济分析. 暖通空调. 1997,27(6):65~67
15 王洋. 单、双级混合式热泵供暖系统流程实验研究. 哈尔滨工业大学硕士论文. 2003:13~18
16 王洋,江辉民,马最良等. 单、双级混合式热泵供热系统总制热能效比的研究. 暖通空调. 2004,34(11):1~4
17 王洋,江辉民,马最良等. 混合式热泵系统在 “三北”地区应用前景分析. 哈尔滨工业大学学报. 2006,38(2):208~211
18 闻新,张洪钺,周露. 控制系统的故障诊断和容错控制. 北京:机械工业出版社. 2000:1~21
19 A. L. Dexter and M. Benouarets. Model-based Fault Diagnosis Using Fuzzy Matching. Trans. IEEE on system. Man and Cybernetics-Part A. 1997, 27(5): 673~682
20 A. L. Dexter and D. Ngo. Fault Diagnosis in Large-scale Air-conditioning Systems. Proc. IFAC Symposium Saferocess’97. 1997: 737~747
21 A. S. Glass and J. Todli. Testing Qualitative Model-based Fault Detection for Air-handling Units Using Building Operational Data. Technical Papers of IEA Annex 25, VTT Building Technology. 1997: 37~46
22 M. Newton. Fault Diagnosis in Uncertain Systems Using Neuron-fuzzy Modeling. D. Phil. Thesis, University of Oxford. 1997: 84~116
23 J. Hyvarien. Building Optimization and Fault Diagnosis (BOFD) System Concept. Collective Document. IEA Annex 25, VTA, Finland. 1993: 67~80
24 H. Yoshida, T. Twami and H. Yuzan. Implementing Faults in a VAV System of R&D Center of Tokyo Electric Power Company. IEA Annex 34, Computer Aided Fault Detection and Diagnosis (Working Paper), CO. USA. 1997: 42~53
25 S. Katipamula, R. G. Pratt, D.P. Chassin, et al. Automated Fault Detection and Diagnostic for Out-air Ventilation Systems and Economizes: Methodology and Result from Field Testing. ASHRAE Transaction. 1999, 105 (1): 555~567
26 T. I. Saltsburg and R. C. Diamond. Fault Detection in HVAC Using Model-based Free-forward Control. Energy and Building. 2001, (33): 403~415
27 C. Ghiaus. Fault Diagnosis of Air-conditioning Systems Based on Qualitative Bond Graph. Energy and Building. 2001, (33): 221~232
28 J. Brodrick. Energy Consumption Characteristics of Commercial Building HVAC System. Volume 3: Energy Savings Potential, DOE. July, 2002
29 马最良,姚杨,王洋,江辉民. 混合式热泵系统中空气源热泵的实验研究. 哈尔滨工业大学学报. 2005,37(2):164~166
30 罗锦,孟晨,苏振中. 故障诊断技术的发展和展望. 自动化与仪器仪表. 2003,106(2):1~2
31 黄文虎,夏松波,刘瑞岩等. 设备故障诊断原理、技术及应用. 北京:科学出版社. 1996:1~16
32 龙浩,魏建顺,王新民等. 基于专家系统的飞机燃油故障诊断方法研究. 计算机测量与控制. 2005,13(5):403~405
33 姜巍,张卫宁. 基于小波变换的柴油机故障智能检测新方法. 控制工程. 2005,12(3):277~280
34 周全,赵书文,文伟军. 基于 BP 网络的雷达故障诊断. 火控雷达技术. 2005,34(6):77~79
35 史永胜,宋云雪. 用遗传算法改进的 BP 模型在刹车系统诊断中的应用研究. 飞行设计. 2005,(1):10~13
36 张晓梅,姜兴渭. 基于动态模糊观测器的故障诊断方法研究. 航空学报. 2004,25(1):49~50
37 宋彤,孙增国,冯冲. 神经网络和模糊系统在故障诊断中的应用. 计算技术与自动化. 2004,23(2):31~34
38 王悦民,潜伟健. 故障诊断技术方法综述及展望. 海军工程学院学报. 1996,77(4):96~103
39 史忠植. 高级人工智能. 北京:科学出版社. 1998:1~12
40 李志生,张国强,刘建龙. 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
41 查金荣译. 人工智能在化学工程中的应用. 北京:中国石化出版社. 1994:1~9
42 M. S. Fox, S. Lowenfeld, P. Kleinosky. Techniques for Sensor-based Diagnosis. Proceedings of IJCAI-83: 158~163
43 J. Pooley. Embedded Expert System for Space Shuttle Main Engine Maintenance. N88~163
44 明志茂,徐永成,陶利民等. 基于规则的某型直升机故障诊断专家系统研究. 航空计算技术. 2004,34(3):61~64
45 李占山,姜云飞. 基于模型诊断推理的回顾与展望. 计算机科学. 1998,25(6):54~57
46 J. de Kleer, J. Brown. A Qualitative Physics Based on Confluences. Artificial Intelligence. 1984, 24(1): 74~84
47 R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence. 1984, 24(3): 347~410
48 R. Reiter. A Theory of Diagnosis from First Principle. Artificial Intelligence. 1987, 1(32): 57~96
49 J. D. Kleer, B. C. Williams. Diagnosing Multiple Faults. Artificial Intelligence. 1987, 1(32): 97~130
50 A. N. Kumar. Function-Based Reasoning: An Introduction. Applied Artificial Intelligence. 1994, 2(8): 246~260
51 B. Chandrasekaran. Function-Based Reasoning: Brief Historical Perspective. Applied Artificial Intelligence. 1994, 2(8): 261~270
52 R. Hawkins. Function-Based Modeling and Troubleshooting. Applied Artificial Intelligence. 1994, 2(8): 298~312
53 王巍. 基于模型残差分析的诊断技术研究及其在航天器中的应用. 哈尔滨工业大学博士论文. 1999:1~21
54 陈世福,陈兆乾. 人工智能与知识工程. 南京:南京大学出版社. 1997:23~37
55 朱博,胡燕,赵永标. 人工神经网络在故障诊断系统中的应用. 舰船电子工程. 2005,25(1):91~95
56 潘昊,江朝华,钟珞. 基于神经网络的故障诊断应用研究. 微机发展. 2004,14(12):30~35
57 郭秀荣,陆怀民,赵志国. 人工神经网络技术在机械故障诊断中的应用. 林业机械与木工设备. 2003,31(12):46~47
58 朱学军,陈宇基. 基于 FTA 的智能型故障诊断方法研究. 微计算机信息. 2005,21(6):123~124
59 Teague. Diagnosis Procedure for Fault Tree Analysis. Proc. of Joint American Control Conference. 1988, FA-2C
60 刘远超. 浅谈故障树分析方法及其在热电厂中的应用. 东北电力技术. 2004,(7):28~30
61 李霁红,康锐,贾颖. 复杂系统的模糊故障诊断方法研究. 系统工程与电子技术. 2005,27(7):1322~1324
62 王翔. 模糊专家诊断系统在状态监测中的应用探讨. 湖南电力. 2005,25(2):35~37
63 米东,徐章遂,王平. 模糊信息融合及其在发动机失火故障诊断中的应用. 河北工业大学学报. 2005,34(1):65~69
64 周志英. 模糊数学在汽车故障诊断中的应用. 长沙大学学报. 2005,19(2):19~21
65 M. Dohnal. An Analysis of Symbolic and Fuzzy Knowledge and Its Engineering Application. Engineering Application of Artificial Intelligence. 1993, 6 (1): 49~56
66 邓聚龙. 灰色系统基本方法. 武汉:华中理工大学出版社. 1988:1~7
67 王振鑫,田融. 灰色系统理论简介. 内蒙古电力技术. 1997,2:70~72
68 彭玉华. 小波变换与工程应用. 北京:科学出版社. 1999:1~10
69 Y. Tsuge, K. Hiratsuka. A Fault Detection and Diagnosis for the Continuous Process with Load-fluctuations Using Orthogonal Wavelets. Computers and Chemical Engineering. 2000, (24): 761~767
70 N. G. Nikolaou and I. A. Antoniadis. Rolling Element Bearing Fault Diagnosis Using Wavelet Packets. NDT&E International. 2002, (35): 197~205
71 王建民,张雅丽. 基于小波模型的故障诊断法. 河北工业大学学报. 2002,31(1):15~19
72 苏厚军,杨家军,王润卿. 基于小波分析的信号检测研究与应用. 武汉理工大学学报(信息与管理工程版). 2005,27(1):15~17
73 吕锋,孙杨,文成林等. 基于小波分析的电机故障振声诊断方法. 电机与控制学报. 2004,8(4):322~328
74 J. H.Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press. 1975
75 李敏强,寇纪凇. 遗传算法的基本理论与应用. 北京:科学出版社. 2002:1~24
76 姚文俊. 基于遗传算法的故障诊断的研究. 微计算机应用. 2004, 25(3):280~283
77 徐波等. 复杂系统的智能故障诊断. 信息与控制. 2004,33(1):56~59
78 徐玉秀,邢刚,原培新. 基于专家系统与神经网络集成的故障诊断的应用研究. 振动与冲击. 2001,20(1):41~43
79 蒋东翔,倪维斗. 大型汽轮发电机组混合智能诊断方法的研究. 清华大学学报. 1999,39(3):75~78
80 毕天姝,倪以信,吴复立等. 基于混合神经网络和遗传算法的故障诊断系统.现代电力. 2005,22(1):31~36
81 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
82 A. Byung-Cheon, J. W. Mitchell and B.D. McIntosh. Model-based Fault Detection and Diagnosis for Cooling Towers. ASHRAE Transaction. 2001, 107 (1): 839~846
83 吴今培. 智能故障诊断技术的发展和展望. 振动、测试与诊断. 1999,19(2):79~86
84 P. B. Usoro, I. C. Schick and S. Negahdaripour. An Innovation-based Methodology for HVAC System Fault Detection. Journal of Dynamic Systems, Measurement and Control, Transaction of the ASME. 1985, (107): 248~285
85 J. S. Haberl and D. E. Claridge. An Expert System for Building Energy Consumption Analysis: Prototype Results. ASHRAE Transaction. 1987, 93 (1): 979~997
86 马吉民. 设备故障诊断修理专家系统开发工具研究及其在制冷系统中的应用. 南京工程兵工程学院硕士论文. 1989:1~20
87 J. E. Braun. Automated Fault Detection and Diagnostics for the HVAV&R Industry. HVAC&R Research. 2001, 5(2): 85~86
88 X. Li. Development of a Fault Diagnosis Method for Heating System Using Neural Networks. ASHRAE Trans. 1996, 1 (102): 607~613
89 X. Li. A Neural Network Prototype for Fault Detection and Diagnostic of Heating Systems. ASHRAE Transaction. 1997, 1 (103): 634-644
90 J. C. Visier, V. N. Hossein and P. Corraies. A Fault Detection Tool for School Buildings. ASHRAE Transactions. 1999, 1 (105): 534~554
91 朱云,郑建英. 基于实时监测的管网供热损耗模型及分析系统. 仪器仪表学报. 2002,3(13)增刊:919~921
92 王福斌,李志. 建立采暖炉汽水冲击故障诊断系统的可行性分析. 区域供热. 2004,(6):12~15
93 吴昊,江志斌. 基于规则的专家系统在制冷空调故障分析中的应用. 制冷学报. 2001,(1):63~68
94 吴勇华. 制冷机泄漏的诊断与预报. 暖通空调. 2001,34(4):102~104
95 余锋,程大章. 一种基于神经网络的建筑设备故障诊断模型. 智能建筑与城市信息. 2003,82(9):40~42
96 王志毅,谷波,黎远光. 小波变换应用于空调制冷机组故障先兆预测. 暖通空调. 2001,34(10):117~120
97 卞荷洁,谷波,黎远光. 基于概率神经网络的制冷空调系统故障诊断分析. 上海交通大学学报. 2004,38(10):1613~1622
98 Setting, Operation and Reparation Handbook of RTHB Screw Chiller, TRAIN CO. 1999:79~85
99 Setting, Operation and Reparation Handbook of Close Scroll Chiller of XL. CARRIER CO. 1999:75~83
100 M. C. Comstock, J. E. Braun and E. A. Groll. The Sensitivity of Chiller Performance to Common Faults. International Journal of HVAC&R Research. 2001, 7(3): 86~86
101 刘用鹿,谢军龙,沈国民. 换热器堵塞故障的诊断与分析. 建筑热能通风空调. 2002,(6):45~47
102 王志毅,谷波,江国和,郑钢. 二级模糊综合评判方法在制冷机故障诊断中的应用. 暖通空调. 2004,34(1):85~88
103 Y. Jiang, Y.Yao, S. Deng, et al. Apply Grey Forcasting to Predicting the Operating Energy Performance of Air Cooled Water Chillers. International Journal of Refrigeration. 2004, (27): 385~392
104 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,(2):25~27
105 S. H. Karki and S. J. Karjalainen. Performance Factors as a Basis of Practical Fault Detection and Diagnostic Methods for Air-handling Units. ASHRAE Transactions. 1999, 105 (1): 1069~1077
106 D. Ngo and A. L. Dexter. A Robust Model-based Approach to Diagnosing Faults in Air-handling Units. ASHRAE Transactions. 1999, 105 (1): 1078~1086
107 J. M. House, W. Y. Lee and D. R. Shin. Classification Techniques for Fault Detection and Diagnosis of an Air-handling Unit. ASHRAE Transactions. 1999, 105 (1): 1087~1097
108 H. Yoshida and S. Kumar. Development of ARX Model Based Off-line FDD Technique for Energy Efficient Buildings. Renewable Energy. 2001, (22): 53~59
109 J. E. Pakanen, T. Sundquist. Automation-assisted Fault Detection of an Air-handling Unit; Implementing the Method in a Real Building. Energy and Buildings. 2003, (35): 193~202
110 J. Pakanen. Demonstrating a Fault Diagnostic Method in an Automated Computer-controlled HVAC Process. Technical Research Center of Finland, VTT Publications, Espoo,Vol. 443, 2001, 45pp. +app. 2pp
111 W. Lee, J. M. House and N. Kyong. Subsystem Level Fault Diagnosis of a Building’s Air-handling Unit Using General Regression Neural Networks. Applied Energy. 2004, (77): 153~170
112 江亿,朱伟峰. 暖通空调系统传感器的故障诊断. 清华大学学报(自然科学版). 1999,39(12):54~56
113 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
114 S. Wang and Y. Chen. Sensor Validation and Reconstruction for Building Central Chilling Systems Based on Principal Component Analysis. Energy Conversion & Management. 2004, (45): 673~695
115 惠广海,晋欣桥,杜志敏. 变流量冷媒水系统温度计及流量传感器故障诊断. 建筑热能通风空调. 2003,22(6):13~18
116 S. Wang, X. Fu. AHU Sensor Fault Diagnosis Using Principal Component Analysis Method. Energy and Buildings. 2004, (36): 147~160
117 S. Wang, X. Fu. Detection and Diagnosis of AHU Sensor Faults Using Principal Component Analysis Method. Energy Conversion & Management. 2004, (45): 2667~2686
118 周强华. 建筑空调的故障诊断研究. 清华大学硕士学位论文. 1995:1~60
119 黄生琪,周菊华. 浅谈风机运行故障诊断方法. 通风除尘. 1996,(1):46~48
120 A. L. Dexter. Control and Fault Detection in Buildings. The 3rd International Symposium on Heating, Ventilation and Air-conditioning. Shenzhen, China. 1999: 869~879
121 C. Y. Han, X. F. Xiao and C. J. Ruther. Fault Detection and Diagnosis of HVAC System. ASHRAE Transaction. 1999, 105 (1): 568~578
122 X.F. Liu and A. L. Dexter. Fault-tolerant Supervisory Control of VAV Air-conditioning Systems. Energy and Building. 2001, (33): 379~389
123 A. L. Dexter and D. Ngo. Fault Diagnosis in Air-conditioning System: a Multi-step Fuzzy Model-based Approach. International Journal of HVAC&R Research. 2001, 1(7): 83~100
124 王志毅,伍星,谷波. 基于 Internet & Intranet 技术和 CERNET 建立集中空调监控和故障诊断系统初探. 暖通空调. 2002,32 (6): 81~82
125 王志毅,谷波. 神经网络故障诊断技术应用在暖通空调系统的可实现性. 建筑热能通风空调. 2002,(1):35~38
126 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学位论文. 2002:113~131
127 B. Yu, H. C. Dolf, V. Paassen, et al. General Modeling for Model-based FDD on Building HVAC System. Simulation Practice and Theory. 2002, (9): 387~397
128 S. Wang and Z. Jiang. Valve Fault Detection and Diagnosis Based on CMAC Neural Networks. Energy and Buildings. 2004, (36): 599~610
129 李德英,谢慧. 空调系统故障诊断的广义故障树知识表示方法. 北京建筑工程学院学报. 2005,21(2):1~6
130 杨欣斌. 故障诊断在智能建筑中的应用研究. 西安交通大学硕士学位论文. 2001:21~26
131 马最良,杨自强,姚杨等. 空气源热泵冷热水机组在寒冷地区应用的分析. 暖通空调,2001,31(3):28~31
132 王洋,江辉民,马最良等. 单、双级混合式热泵系统切换条件的实验研究. 暖通空调,2005,35(2):1~3
133 朱培根,茅靳丰等. 水源热泵空调系统在地下建筑中应用的研究. 暖通空调. 2002,32(6):90~91
134 黄嘉骏. 水源热泵空调系统及其在建筑中的应用. 广东建材. 2006,(8):178~180
135 马最良. 热泵技术(上). 电力需求侧管理. 2003,5(5):58~60
136 D. E. Stouppe and T. S. Lau. Air Conditioning and Refrigeration Equipment Failures. National Engineer, 1989, 93(9): 14~17
137 M. C. Comstock, J. E. Braun and E. A. Groll. A Survey of Common Faults for Chillers. ASHRAE Transaction, 2002, 108(1): 819~825
138 李韵. YCAM-H-600 热泵故障分析及其对策. 暖通空调. 2003,33(2):102~103
139 徐小力,许宝杰,张森. 机械设备趋势预测与神经网络预测方法的研究. 现代机械,2000,(1):62~63
140 胡守仁. 神经网络导论. 长沙:国防科技大学出版社. 1999:1~15
141 D. F. Sprecht. A General Regression Neural Network. IEEE Trans Neural Networks, 1991, (2): 568~576
142 D. F. Sprecht. The General Regression Neural Network Rediscovered. Neural Networks, 1993, (6): 1033~1034
143 韩力群. 人工神经网络理论、设计及应用. 北京:化学工业出版社,2002:24~46
144 飞思科技产品研发中心. 神经网络理论与 MATLAB 实现. 北京:电子工业出版社,2005:257~297
145 姜益强,姚杨,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
146 王伟,姚杨,马最良. 基于 BP 神经网络的压缩机性能预测模型的建立. 流体机械,2005,33(9):21~24
147 赵闯,刘凯,李电生. 基于广义回归神经网络的货运量预测. 铁道学报,2004,26(1):12~15
148 杨丰国,谢峰. 水源热泵系统几种故障及解决办法. 制冷与空调,2005, 5(4):99~101
149 邹新生. 离心冷水机组常见故障及其处理. 冷藏技术,1998,85(4):43~45
150 翟家佩,宣苓柱. 螺杠式冷水机组常见故障及其处理. 压缩机技术,1996, 138(4):37~39
151 郑洁,杨朝杰,周玉礼等. 螺杠式冷水机组的故障模拟及诊断软件的研究. 建筑热能通风空调,2005,24(6):19~23
152 王玉贵,夏畹. 中小型空调器制冷剂最佳充注量的确定. 暖通空调,1999,29(1):41~43
153 M. S. Breuker and J. E. Braun. Evaluating the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment. International Journal of HVAC&R Research, 1998, 4(4): 401~424
154 W. Wang, Z. Ma and Y. Jiang. Field Test Investigation of a Double-stage Coupled Heat Pumps Heating System for Cold Regions. International Journal of Refrigeration, 2005, (28): 672~679
155 D. H. Bultman, L. C. Burmeeister and V. Bortone. Vapor Compression Refrigeration Performance Degradation due to Condenser Airflow Blockage. In Proc. of the National Heat Pump Transfer Conference, 1993, New York: ASME
156 B. Krafthefer, C. Rask and D. R. Bonne. Air Conditioning and Heat Pump Operation Cost Savings by Maintaining Coil Cleanliness. ASHRAE Transaction, 1987, 93(1): 1458~1473
157 T. M. Rossi and J. E. Braun. Minimizing Operation Costs of Vapor Compression Equipment with Optical Service Scheduling. International Journal of HVAC&R Research, 1996, 3(1): 19~37
158 井上宇市,著. 空气调节手册. 范存养,钱以明,秦慧敏等译. 中国建筑工业出版社,1986:236~241
159 王剑锋,陈光明. 空气源热泵冬季结霜特性研究. 制冷,1997,58(1):8~11
160 闻新,周露,李翔等. MATLAB 神经网络仿真与应用. 北京:科学出版社,2003:285~297
161 黄虎,束鹏程,李志浩. 风冷热泵冷热水机组结霜工况下运行特性的实验研究. 流体机械,1998,26(12):43~46
162 韩志涛,姚杨,马最良等. 空气源热泵误除霜特性的实验研究. 暖通空调,2006,36(2):15~19
163 韩慧秋,董威. 空气源热泵除霜问题分析. 长春下程学院学报(自然科学版),2003, 4(1):14~16
164 姚杨. 空气源热泵冷热水机组冬季结霜工况的模拟与分析. 哈尔滨工业大学博士论文,2002:49~74
165 陈颖,李筱萍. 热泵型空调器结霜除霜的判定. 暖通空调,2001,(6):44~47
166 张哲,厉彦忠,田津津等. 风冷热泵除霜的实验研究. 低温与超导,2001,31(1):36~38
167 S. Z. Zhang. Study on Frosting Characteristics of an Evaporator Using R407c as Refrigerant. International Symposium of Air-conditioning in High-rise Building's 2000, Shanghai 2000, 269~273
168 罗鸣,谢军龙,沈国民. 风冷热泵冷热水机组除霜研究. 建筑热能通风空调,2002,(6):15~17
169 黄东,袁秀玲,张波等. 风机提前启动对风冷热泵冷热水机组除霜的影响. 西安交通大学学报,2004,38(5):448~451
170 Y. Chen and S. Wang. Sensor Fault Tolerant Control of Outdoor Air Flow Based on Neural Networks. Proc, Indoor Air Quality, Ventilation and Energy Conservation in Buildings, 2001, (2): 231~242
171 Y. Chen, S. Wang. Effects of Sensor Faults of BMS on Energy Efficiency of Building Air-conditioning System in Subtropical Climate. Asian-pacific Conference on the Building & Environment, Singapore, 2001, (1): 231~241
172 李冬辉,徐津津. 基于改进 BP 神经网络的空调系统传感器故障诊断. 低压电器. 2005,(12):24~26
173 钮永胜,赵新民,孙金玮. 传感器故障诊断方法研究. 航天控制,1996,(4):34~37
174 江梅. 基于神经网络的传感器故障检测与容错. 东北大学硕士学位论文,2003:23~26
175 苏红,田沛,常晓权等. 基于 RBF 网络的水位传感器故障诊断方法. 华北电力大学学报,2005,32(3):47~51
176 吴信东,邹燕. 专家系统技术. 北京:电子工业出版社,1988:1~9
177 尹朝庆,尹皓. 人工智能与专家系统,北京:中国水利水电出版社,2002:314~317
178 余涛,王晶,沈善德等. 分布式水电机组故障诊断集成专家系统的研究. 动力工程,2002,22(4):1902~1907
179 徐海银,周祖德,袁楚明. 现代制造系统分布式智能监控和诊断理论与技术. 机械与电子,1999,(3):19~21
180 施国洪,夏敬华. 面向复杂设备的分布式故障诊断研究. 中国安全科学学报,2000,10(2):75~79
181 M. S. Breuker. Evaluation the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment. International Journal of HVAC&R Research,1998, 4 (4): 401~424
2 陈清泰. 中国能源战略走一步看一步不可取. 能源战略和改革国际研讨会. 北京,2003 年 11 月
3 http://www.cenews.com.cn/news/2005-08-19/48586.php
4 朗四维. 中国建筑环境设备发展动向. 中日建筑环境设备高级论坛论文集. 上海,2003:27~50
5 陈滨,孙鹏,丁颖慧等. 住宅能源消费结构及自然能源应用. 暖通空调. 2005,35(3):45~50
6 马最良. 替代寒冷地区传统供暖的新型热泵供暖方式的探讨. 暖通空调新技术 3. 2001:31~35
7 陆亚俊,马最良,姚扬. 空调工程中的制冷技术. 哈尔滨:哈尔滨工程大学出版社. 1997:217~229
8 喻银平,马最良. 双级耦合式热泵系统在寒冷地区应用的可行性分析. 电力需求测管理. 2002,4(2):39~42
9 龙惟定,白玮,张蓓红. 中国住宅空调的未来发展. 暖通空调. 2002,32(4):43~47
10 沈明,宋之平. 空气源热泵应用范围北扩的可能性分析及其技术措施研究. 暖通空调. 2002,32(6):37~39
11 马最良,杨自强,姚杨等.空气源热泵冷热水机组在寒冷地区应用的可行性分析.暖通空调. 2001,34(3):28~31
12 喻银平. 双级耦合热泵系统模拟研究. 哈尔滨工业大学工学硕士学位论文. 2002:15~40
13 连之伟,张欧. 风冷热泵在西安地区的运行效果测定及分析. 暖通空调. 1998,28(5):17~20
14 张积太. 空气源热泵冷热水机组在胶东地区的设计尝试及技术经济分析. 暖通空调. 1997,27(6):65~67
15 王洋. 单、双级混合式热泵供暖系统流程实验研究. 哈尔滨工业大学硕士论文. 2003:13~18
16 王洋,江辉民,马最良等. 单、双级混合式热泵供热系统总制热能效比的研究. 暖通空调. 2004,34(11):1~4
17 王洋,江辉民,马最良等. 混合式热泵系统在 “三北”地区应用前景分析. 哈尔滨工业大学学报. 2006,38(2):208~211
18 闻新,张洪钺,周露. 控制系统的故障诊断和容错控制. 北京:机械工业出版社. 2000:1~21
19 A. L. Dexter and M. Benouarets. Model-based Fault Diagnosis Using Fuzzy Matching. Trans. IEEE on system. Man and Cybernetics-Part A. 1997, 27(5): 673~682
20 A. L. Dexter and D. Ngo. Fault Diagnosis in Large-scale Air-conditioning Systems. Proc. IFAC Symposium Saferocess’97. 1997: 737~747
21 A. S. Glass and J. Todli. Testing Qualitative Model-based Fault Detection for Air-handling Units Using Building Operational Data. Technical Papers of IEA Annex 25, VTT Building Technology. 1997: 37~46
22 M. Newton. Fault Diagnosis in Uncertain Systems Using Neuron-fuzzy Modeling. D. Phil. Thesis, University of Oxford. 1997: 84~116
23 J. Hyvarien. Building Optimization and Fault Diagnosis (BOFD) System Concept. Collective Document. IEA Annex 25, VTA, Finland. 1993: 67~80
24 H. Yoshida, T. Twami and H. Yuzan. Implementing Faults in a VAV System of R&D Center of Tokyo Electric Power Company. IEA Annex 34, Computer Aided Fault Detection and Diagnosis (Working Paper), CO. USA. 1997: 42~53
25 S. Katipamula, R. G. Pratt, D.P. Chassin, et al. Automated Fault Detection and Diagnostic for Out-air Ventilation Systems and Economizes: Methodology and Result from Field Testing. ASHRAE Transaction. 1999, 105 (1): 555~567
26 T. I. Saltsburg and R. C. Diamond. Fault Detection in HVAC Using Model-based Free-forward Control. Energy and Building. 2001, (33): 403~415
27 C. Ghiaus. Fault Diagnosis of Air-conditioning Systems Based on Qualitative Bond Graph. Energy and Building. 2001, (33): 221~232
28 J. Brodrick. Energy Consumption Characteristics of Commercial Building HVAC System. Volume 3: Energy Savings Potential, DOE. July, 2002
29 马最良,姚杨,王洋,江辉民. 混合式热泵系统中空气源热泵的实验研究. 哈尔滨工业大学学报. 2005,37(2):164~166
30 罗锦,孟晨,苏振中. 故障诊断技术的发展和展望. 自动化与仪器仪表. 2003,106(2):1~2
31 黄文虎,夏松波,刘瑞岩等. 设备故障诊断原理、技术及应用. 北京:科学出版社. 1996:1~16
32 龙浩,魏建顺,王新民等. 基于专家系统的飞机燃油故障诊断方法研究. 计算机测量与控制. 2005,13(5):403~405
33 姜巍,张卫宁. 基于小波变换的柴油机故障智能检测新方法. 控制工程. 2005,12(3):277~280
34 周全,赵书文,文伟军. 基于 BP 网络的雷达故障诊断. 火控雷达技术. 2005,34(6):77~79
35 史永胜,宋云雪. 用遗传算法改进的 BP 模型在刹车系统诊断中的应用研究. 飞行设计. 2005,(1):10~13
36 张晓梅,姜兴渭. 基于动态模糊观测器的故障诊断方法研究. 航空学报. 2004,25(1):49~50
37 宋彤,孙增国,冯冲. 神经网络和模糊系统在故障诊断中的应用. 计算技术与自动化. 2004,23(2):31~34
38 王悦民,潜伟健. 故障诊断技术方法综述及展望. 海军工程学院学报. 1996,77(4):96~103
39 史忠植. 高级人工智能. 北京:科学出版社. 1998:1~12
40 李志生,张国强,刘建龙. 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
41 查金荣译. 人工智能在化学工程中的应用. 北京:中国石化出版社. 1994:1~9
42 M. S. Fox, S. Lowenfeld, P. Kleinosky. Techniques for Sensor-based Diagnosis. Proceedings of IJCAI-83: 158~163
43 J. Pooley. Embedded Expert System for Space Shuttle Main Engine Maintenance. N88~163
44 明志茂,徐永成,陶利民等. 基于规则的某型直升机故障诊断专家系统研究. 航空计算技术. 2004,34(3):61~64
45 李占山,姜云飞. 基于模型诊断推理的回顾与展望. 计算机科学. 1998,25(6):54~57
46 J. de Kleer, J. Brown. A Qualitative Physics Based on Confluences. Artificial Intelligence. 1984, 24(1): 74~84
47 R. Davis. Diagnostic Reasoning Based on Structure and Behavior. Artificial Intelligence. 1984, 24(3): 347~410
48 R. Reiter. A Theory of Diagnosis from First Principle. Artificial Intelligence. 1987, 1(32): 57~96
49 J. D. Kleer, B. C. Williams. Diagnosing Multiple Faults. Artificial Intelligence. 1987, 1(32): 97~130
50 A. N. Kumar. Function-Based Reasoning: An Introduction. Applied Artificial Intelligence. 1994, 2(8): 246~260
51 B. Chandrasekaran. Function-Based Reasoning: Brief Historical Perspective. Applied Artificial Intelligence. 1994, 2(8): 261~270
52 R. Hawkins. Function-Based Modeling and Troubleshooting. Applied Artificial Intelligence. 1994, 2(8): 298~312
53 王巍. 基于模型残差分析的诊断技术研究及其在航天器中的应用. 哈尔滨工业大学博士论文. 1999:1~21
54 陈世福,陈兆乾. 人工智能与知识工程. 南京:南京大学出版社. 1997:23~37
55 朱博,胡燕,赵永标. 人工神经网络在故障诊断系统中的应用. 舰船电子工程. 2005,25(1):91~95
56 潘昊,江朝华,钟珞. 基于神经网络的故障诊断应用研究. 微机发展. 2004,14(12):30~35
57 郭秀荣,陆怀民,赵志国. 人工神经网络技术在机械故障诊断中的应用. 林业机械与木工设备. 2003,31(12):46~47
58 朱学军,陈宇基. 基于 FTA 的智能型故障诊断方法研究. 微计算机信息. 2005,21(6):123~124
59 Teague. Diagnosis Procedure for Fault Tree Analysis. Proc. of Joint American Control Conference. 1988, FA-2C
60 刘远超. 浅谈故障树分析方法及其在热电厂中的应用. 东北电力技术. 2004,(7):28~30
61 李霁红,康锐,贾颖. 复杂系统的模糊故障诊断方法研究. 系统工程与电子技术. 2005,27(7):1322~1324
62 王翔. 模糊专家诊断系统在状态监测中的应用探讨. 湖南电力. 2005,25(2):35~37
63 米东,徐章遂,王平. 模糊信息融合及其在发动机失火故障诊断中的应用. 河北工业大学学报. 2005,34(1):65~69
64 周志英. 模糊数学在汽车故障诊断中的应用. 长沙大学学报. 2005,19(2):19~21
65 M. Dohnal. An Analysis of Symbolic and Fuzzy Knowledge and Its Engineering Application. Engineering Application of Artificial Intelligence. 1993, 6 (1): 49~56
66 邓聚龙. 灰色系统基本方法. 武汉:华中理工大学出版社. 1988:1~7
67 王振鑫,田融. 灰色系统理论简介. 内蒙古电力技术. 1997,2:70~72
68 彭玉华. 小波变换与工程应用. 北京:科学出版社. 1999:1~10
69 Y. Tsuge, K. Hiratsuka. A Fault Detection and Diagnosis for the Continuous Process with Load-fluctuations Using Orthogonal Wavelets. Computers and Chemical Engineering. 2000, (24): 761~767
70 N. G. Nikolaou and I. A. Antoniadis. Rolling Element Bearing Fault Diagnosis Using Wavelet Packets. NDT&E International. 2002, (35): 197~205
71 王建民,张雅丽. 基于小波模型的故障诊断法. 河北工业大学学报. 2002,31(1):15~19
72 苏厚军,杨家军,王润卿. 基于小波分析的信号检测研究与应用. 武汉理工大学学报(信息与管理工程版). 2005,27(1):15~17
73 吕锋,孙杨,文成林等. 基于小波分析的电机故障振声诊断方法. 电机与控制学报. 2004,8(4):322~328
74 J. H.Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press. 1975
75 李敏强,寇纪凇. 遗传算法的基本理论与应用. 北京:科学出版社. 2002:1~24
76 姚文俊. 基于遗传算法的故障诊断的研究. 微计算机应用. 2004, 25(3):280~283
77 徐波等. 复杂系统的智能故障诊断. 信息与控制. 2004,33(1):56~59
78 徐玉秀,邢刚,原培新. 基于专家系统与神经网络集成的故障诊断的应用研究. 振动与冲击. 2001,20(1):41~43
79 蒋东翔,倪维斗. 大型汽轮发电机组混合智能诊断方法的研究. 清华大学学报. 1999,39(3):75~78
80 毕天姝,倪以信,吴复立等. 基于混合神经网络和遗传算法的故障诊断系统.现代电力. 2005,22(1):31~36
81 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
82 A. Byung-Cheon, J. W. Mitchell and B.D. McIntosh. Model-based Fault Detection and Diagnosis for Cooling Towers. ASHRAE Transaction. 2001, 107 (1): 839~846
83 吴今培. 智能故障诊断技术的发展和展望. 振动、测试与诊断. 1999,19(2):79~86
84 P. B. Usoro, I. C. Schick and S. Negahdaripour. An Innovation-based Methodology for HVAC System Fault Detection. Journal of Dynamic Systems, Measurement and Control, Transaction of the ASME. 1985, (107): 248~285
85 J. S. Haberl and D. E. Claridge. An Expert System for Building Energy Consumption Analysis: Prototype Results. ASHRAE Transaction. 1987, 93 (1): 979~997
86 马吉民. 设备故障诊断修理专家系统开发工具研究及其在制冷系统中的应用. 南京工程兵工程学院硕士论文. 1989:1~20
87 J. E. Braun. Automated Fault Detection and Diagnostics for the HVAV&R Industry. HVAC&R Research. 2001, 5(2): 85~86
88 X. Li. Development of a Fault Diagnosis Method for Heating System Using Neural Networks. ASHRAE Trans. 1996, 1 (102): 607~613
89 X. Li. A Neural Network Prototype for Fault Detection and Diagnostic of Heating Systems. ASHRAE Transaction. 1997, 1 (103): 634-644
90 J. C. Visier, V. N. Hossein and P. Corraies. A Fault Detection Tool for School Buildings. ASHRAE Transactions. 1999, 1 (105): 534~554
91 朱云,郑建英. 基于实时监测的管网供热损耗模型及分析系统. 仪器仪表学报. 2002,3(13)增刊:919~921
92 王福斌,李志. 建立采暖炉汽水冲击故障诊断系统的可行性分析. 区域供热. 2004,(6):12~15
93 吴昊,江志斌. 基于规则的专家系统在制冷空调故障分析中的应用. 制冷学报. 2001,(1):63~68
94 吴勇华. 制冷机泄漏的诊断与预报. 暖通空调. 2001,34(4):102~104
95 余锋,程大章. 一种基于神经网络的建筑设备故障诊断模型. 智能建筑与城市信息. 2003,82(9):40~42
96 王志毅,谷波,黎远光. 小波变换应用于空调制冷机组故障先兆预测. 暖通空调. 2001,34(10):117~120
97 卞荷洁,谷波,黎远光. 基于概率神经网络的制冷空调系统故障诊断分析. 上海交通大学学报. 2004,38(10):1613~1622
98 Setting, Operation and Reparation Handbook of RTHB Screw Chiller, TRAIN CO. 1999:79~85
99 Setting, Operation and Reparation Handbook of Close Scroll Chiller of XL. CARRIER CO. 1999:75~83
100 M. C. Comstock, J. E. Braun and E. A. Groll. The Sensitivity of Chiller Performance to Common Faults. International Journal of HVAC&R Research. 2001, 7(3): 86~86
101 刘用鹿,谢军龙,沈国民. 换热器堵塞故障的诊断与分析. 建筑热能通风空调. 2002,(6):45~47
102 王志毅,谷波,江国和,郑钢. 二级模糊综合评判方法在制冷机故障诊断中的应用. 暖通空调. 2004,34(1):85~88
103 Y. Jiang, Y.Yao, S. Deng, et al. Apply Grey Forcasting to Predicting the Operating Energy Performance of Air Cooled Water Chillers. International Journal of Refrigeration. 2004, (27): 385~392
104 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,(2):25~27
105 S. H. Karki and S. J. Karjalainen. Performance Factors as a Basis of Practical Fault Detection and Diagnostic Methods for Air-handling Units. ASHRAE Transactions. 1999, 105 (1): 1069~1077
106 D. Ngo and A. L. Dexter. A Robust Model-based Approach to Diagnosing Faults in Air-handling Units. ASHRAE Transactions. 1999, 105 (1): 1078~1086
107 J. M. House, W. Y. Lee and D. R. Shin. Classification Techniques for Fault Detection and Diagnosis of an Air-handling Unit. ASHRAE Transactions. 1999, 105 (1): 1087~1097
108 H. Yoshida and S. Kumar. Development of ARX Model Based Off-line FDD Technique for Energy Efficient Buildings. Renewable Energy. 2001, (22): 53~59
109 J. E. Pakanen, T. Sundquist. Automation-assisted Fault Detection of an Air-handling Unit; Implementing the Method in a Real Building. Energy and Buildings. 2003, (35): 193~202
110 J. Pakanen. Demonstrating a Fault Diagnostic Method in an Automated Computer-controlled HVAC Process. Technical Research Center of Finland, VTT Publications, Espoo,Vol. 443, 2001, 45pp. +app. 2pp
111 W. Lee, J. M. House and N. Kyong. Subsystem Level Fault Diagnosis of a Building’s Air-handling Unit Using General Regression Neural Networks. Applied Energy. 2004, (77): 153~170
112 江亿,朱伟峰. 暖通空调系统传感器的故障诊断. 清华大学学报(自然科学版). 1999,39(12):54~56
113 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
114 S. Wang and Y. Chen. Sensor Validation and Reconstruction for Building Central Chilling Systems Based on Principal Component Analysis. Energy Conversion & Management. 2004, (45): 673~695
115 惠广海,晋欣桥,杜志敏. 变流量冷媒水系统温度计及流量传感器故障诊断. 建筑热能通风空调. 2003,22(6):13~18
116 S. Wang, X. Fu. AHU Sensor Fault Diagnosis Using Principal Component Analysis Method. Energy and Buildings. 2004, (36): 147~160
117 S. Wang, X. Fu. Detection and Diagnosis of AHU Sensor Faults Using Principal Component Analysis Method. Energy Conversion & Management. 2004, (45): 2667~2686
118 周强华. 建筑空调的故障诊断研究. 清华大学硕士学位论文. 1995:1~60
119 黄生琪,周菊华. 浅谈风机运行故障诊断方法. 通风除尘. 1996,(1):46~48
120 A. L. Dexter. Control and Fault Detection in Buildings. The 3rd International Symposium on Heating, Ventilation and Air-conditioning. Shenzhen, China. 1999: 869~879
121 C. Y. Han, X. F. Xiao and C. J. Ruther. Fault Detection and Diagnosis of HVAC System. ASHRAE Transaction. 1999, 105 (1): 568~578
122 X.F. Liu and A. L. Dexter. Fault-tolerant Supervisory Control of VAV Air-conditioning Systems. Energy and Building. 2001, (33): 379~389
123 A. L. Dexter and D. Ngo. Fault Diagnosis in Air-conditioning System: a Multi-step Fuzzy Model-based Approach. International Journal of HVAC&R Research. 2001, 1(7): 83~100
124 王志毅,伍星,谷波. 基于 Internet & Intranet 技术和 CERNET 建立集中空调监控和故障诊断系统初探. 暖通空调. 2002,32 (6): 81~82
125 王志毅,谷波. 神经网络故障诊断技术应用在暖通空调系统的可实现性. 建筑热能通风空调. 2002,(1):35~38
126 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学位论文. 2002:113~131
127 B. Yu, H. C. Dolf, V. Paassen, et al. General Modeling for Model-based FDD on Building HVAC System. Simulation Practice and Theory. 2002, (9): 387~397
128 S. Wang and Z. Jiang. Valve Fault Detection and Diagnosis Based on CMAC Neural Networks. Energy and Buildings. 2004, (36): 599~610
129 李德英,谢慧. 空调系统故障诊断的广义故障树知识表示方法. 北京建筑工程学院学报. 2005,21(2):1~6
130 杨欣斌. 故障诊断在智能建筑中的应用研究. 西安交通大学硕士学位论文. 2001:21~26
131 马最良,杨自强,姚杨等. 空气源热泵冷热水机组在寒冷地区应用的分析. 暖通空调,2001,31(3):28~31
132 王洋,江辉民,马最良等. 单、双级混合式热泵系统切换条件的实验研究. 暖通空调,2005,35(2):1~3
133 朱培根,茅靳丰等. 水源热泵空调系统在地下建筑中应用的研究. 暖通空调. 2002,32(6):90~91
134 黄嘉骏. 水源热泵空调系统及其在建筑中的应用. 广东建材. 2006,(8):178~180
135 马最良. 热泵技术(上). 电力需求侧管理. 2003,5(5):58~60
136 D. E. Stouppe and T. S. Lau. Air Conditioning and Refrigeration Equipment Failures. National Engineer, 1989, 93(9): 14~17
137 M. C. Comstock, J. E. Braun and E. A. Groll. A Survey of Common Faults for Chillers. ASHRAE Transaction, 2002, 108(1): 819~825
138 李韵. YCAM-H-600 热泵故障分析及其对策. 暖通空调. 2003,33(2):102~103
139 徐小力,许宝杰,张森. 机械设备趋势预测与神经网络预测方法的研究. 现代机械,2000,(1):62~63
140 胡守仁. 神经网络导论. 长沙:国防科技大学出版社. 1999:1~15
141 D. F. Sprecht. A General Regression Neural Network. IEEE Trans Neural Networks, 1991, (2): 568~576
142 D. F. Sprecht. The General Regression Neural Network Rediscovered. Neural Networks, 1993, (6): 1033~1034
143 韩力群. 人工神经网络理论、设计及应用. 北京:化学工业出版社,2002:24~46
144 飞思科技产品研发中心. 神经网络理论与 MATLAB 实现. 北京:电子工业出版社,2005:257~297
145 姜益强,姚杨,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
146 王伟,姚杨,马最良. 基于 BP 神经网络的压缩机性能预测模型的建立. 流体机械,2005,33(9):21~24
147 赵闯,刘凯,李电生. 基于广义回归神经网络的货运量预测. 铁道学报,2004,26(1):12~15
148 杨丰国,谢峰. 水源热泵系统几种故障及解决办法. 制冷与空调,2005, 5(4):99~101
149 邹新生. 离心冷水机组常见故障及其处理. 冷藏技术,1998,85(4):43~45
150 翟家佩,宣苓柱. 螺杠式冷水机组常见故障及其处理. 压缩机技术,1996, 138(4):37~39
151 郑洁,杨朝杰,周玉礼等. 螺杠式冷水机组的故障模拟及诊断软件的研究. 建筑热能通风空调,2005,24(6):19~23
152 王玉贵,夏畹. 中小型空调器制冷剂最佳充注量的确定. 暖通空调,1999,29(1):41~43
153 M. S. Breuker and J. E. Braun. Evaluating the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment. International Journal of HVAC&R Research, 1998, 4(4): 401~424
154 W. Wang, Z. Ma and Y. Jiang. Field Test Investigation of a Double-stage Coupled Heat Pumps Heating System for Cold Regions. International Journal of Refrigeration, 2005, (28): 672~679
155 D. H. Bultman, L. C. Burmeeister and V. Bortone. Vapor Compression Refrigeration Performance Degradation due to Condenser Airflow Blockage. In Proc. of the National Heat Pump Transfer Conference, 1993, New York: ASME
156 B. Krafthefer, C. Rask and D. R. Bonne. Air Conditioning and Heat Pump Operation Cost Savings by Maintaining Coil Cleanliness. ASHRAE Transaction, 1987, 93(1): 1458~1473
157 T. M. Rossi and J. E. Braun. Minimizing Operation Costs of Vapor Compression Equipment with Optical Service Scheduling. International Journal of HVAC&R Research, 1996, 3(1): 19~37
158 井上宇市,著. 空气调节手册. 范存养,钱以明,秦慧敏等译. 中国建筑工业出版社,1986:236~241
159 王剑锋,陈光明. 空气源热泵冬季结霜特性研究. 制冷,1997,58(1):8~11
160 闻新,周露,李翔等. MATLAB 神经网络仿真与应用. 北京:科学出版社,2003:285~297
161 黄虎,束鹏程,李志浩. 风冷热泵冷热水机组结霜工况下运行特性的实验研究. 流体机械,1998,26(12):43~46
162 韩志涛,姚杨,马最良等. 空气源热泵误除霜特性的实验研究. 暖通空调,2006,36(2):15~19
163 韩慧秋,董威. 空气源热泵除霜问题分析. 长春下程学院学报(自然科学版),2003, 4(1):14~16
164 姚杨. 空气源热泵冷热水机组冬季结霜工况的模拟与分析. 哈尔滨工业大学博士论文,2002:49~74
165 陈颖,李筱萍. 热泵型空调器结霜除霜的判定. 暖通空调,2001,(6):44~47
166 张哲,厉彦忠,田津津等. 风冷热泵除霜的实验研究. 低温与超导,2001,31(1):36~38
167 S. Z. Zhang. Study on Frosting Characteristics of an Evaporator Using R407c as Refrigerant. International Symposium of Air-conditioning in High-rise Building's 2000, Shanghai 2000, 269~273
168 罗鸣,谢军龙,沈国民. 风冷热泵冷热水机组除霜研究. 建筑热能通风空调,2002,(6):15~17
169 黄东,袁秀玲,张波等. 风机提前启动对风冷热泵冷热水机组除霜的影响. 西安交通大学学报,2004,38(5):448~451
170 Y. Chen and S. Wang. Sensor Fault Tolerant Control of Outdoor Air Flow Based on Neural Networks. Proc, Indoor Air Quality, Ventilation and Energy Conservation in Buildings, 2001, (2): 231~242
171 Y. Chen, S. Wang. Effects of Sensor Faults of BMS on Energy Efficiency of Building Air-conditioning System in Subtropical Climate. Asian-pacific Conference on the Building & Environment, Singapore, 2001, (1): 231~241
172 李冬辉,徐津津. 基于改进 BP 神经网络的空调系统传感器故障诊断. 低压电器. 2005,(12):24~26
173 钮永胜,赵新民,孙金玮. 传感器故障诊断方法研究. 航天控制,1996,(4):34~37
174 江梅. 基于神经网络的传感器故障检测与容错. 东北大学硕士学位论文,2003:23~26
175 苏红,田沛,常晓权等. 基于 RBF 网络的水位传感器故障诊断方法. 华北电力大学学报,2005,32(3):47~51
176 吴信东,邹燕. 专家系统技术. 北京:电子工业出版社,1988:1~9
177 尹朝庆,尹皓. 人工智能与专家系统,北京:中国水利水电出版社,2002:314~317
178 余涛,王晶,沈善德等. 分布式水电机组故障诊断集成专家系统的研究. 动力工程,2002,22(4):1902~1907
179 徐海银,周祖德,袁楚明. 现代制造系统分布式智能监控和诊断理论与技术. 机械与电子,1999,(3):19~21
180 施国洪,夏敬华. 面向复杂设备的分布式故障诊断研究. 中国安全科学学报,2000,10(2):75~79
181 M. S. Breuker. Evaluation the Performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment. International Journal of HVAC&R Research,1998, 4 (4): 401~424
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