摘要
由于具有节能效果好、热舒适性好、控制灵活和易于拓展等优点,变风量空调系统在高档办公建筑中得到了越来越广泛的应用。变风量空调系统主要由变风量空气处理机组、空气输送管道和变风量空调末端装置构成。变风量空调系统的系统构成比较复杂,对自动控制水平有较高要求,导致变风量空调系统的故障发生比较频繁。故障会增加空调系统能耗,降低热舒适性,加剧空调设备损耗和增加设备维修费用。现代大型建筑大都安装了能源管理与控制系统,能源管理与控制系统保存了大量空调系统运行数据,这些数据为空调系统在线故障检测与诊断研究提供了重要的信息基础。空调系统在线故障检测与诊断研究正逐渐受到人们的重视和关注。因此,研究变风量空调系统在线故障检测与诊断方法,开发变风量空调系统在线故障检测与诊断软件,具有重要的经济意义和工程实际意义。本文从变风量空调系统各主要组成设备的角度出发,深入研究了变风量空调系统在线故障检测与诊断方法。
基于模型的故障诊断方法要求空调部件模型有较高的预测精度。根据空调系统的运行数据调整模型参数,可以提高空调部件模型的预测精度。针对变风量空气处理机组,本文提出了参数自整定空调部件模型。参数自整定空调部件模型利用遗传算法优化模型参数,使模型预测值与系统实测值之间的残差最小。利用实际空调系统的运行数据验证了提出的空调部件模型,验证结果表明参数自整定空调部件模型具有较高的预测精度,可以用于检测变风量空气处理机组故障。针对在实际应用时故障检测阈值的确定比较困难,本文给出了一种故障检测阈值的在线自适应估计方法。采用统计方法确定故障检测阈值,故障检测阈值可以根据空调系统运行条件的变化自动进行更新,有助于提高故障检测结果的准确性。
对于变风量空气处理机组故障检测与诊断而言,每种单一的故障诊断方法均存在缺陷。因此本文将参数自整定空调部件模型与专家规则相融合用于诊断变风量空气处理机组故障,提出了一种变风量空气处理机组在线故障检测与诊断方法。该故障诊断方法利用参数自整定空调部件模型检测变风量空气处理机组故障,利用设计的3个基于规则的故障分类器寻找故障原因。变风量空气处理机组故障检测与诊断方法在实际变风量空调系统上进行了在线应用,通过在实际变风量空调系统上进行在线应用检验了故障诊断方法的有效性
控制图参数对累积和(Cumulative sum, CUSUM)控制图性能有较大影响,然而,在实际应用时CUSUM控制图参数的确定比较困难。本文在深入分析CUSUM控制图参数的基础上,详细介绍了CUSUM控制图参数和工业过程参数的确定方法。本文以一阶自回归过程(First order autoregressive process, AR(1))和一阶移动平均过程(First order moving average process, MA(1))为例,采用数学推理的方法分析了数据自相关性对控制图性能的不利影响。并在此基础上,提出利用残差CUSUM控制图检测变风量空调末端装置故障,残差CUSUM控制图既可以减少空调系统运行过程中瞬变过程对故障检测结果的不利影响,又可以消除数据自相关性对CUSUM控制图性能的不利影响,可以提高故障检测结果的准确性。
本文将残差CUSUM控制图与专家规则相融合用于检测与诊断变风量空调末端装置故障,提出了一种变风量空调末端装置在线故障检测与诊断方法。该故障诊断方法利用残差CUSUM控制图检测变风量空调末端装置故障,利用设计的专家规则和故障分离算法寻找故障原因。变风量空调末端装置故障检测与诊断方法在实际变风量空调系统上进行了在线应用,通过在实际变风量空调系统上进行在线应用检验了故障诊断方法的有效性
在理论研究的基础上,本文开发了变风量空调系统在线故障检测与诊断软件变风量空调系统在线故障检测与诊断软件包括变风量空气处理机组在线故障检测与诊断软件和变风量空调末端装置在线故障检测与诊断软件。变风量空调系统在线故障检测与诊断软件在实际建筑中进行了在线应用。通过在实际建筑中进行在线应用检验了变风量空调系统在线故障检测与诊断软件的可靠性。变风量空调系统在线故障检测与诊断软件可以直观的反映空调设备的运行状态,准确的判断变风量空调系统的故障原因。
Variable air volume (VAV) air-conditioning system is a popular type of heating, ventilation and air-conditioning (HVAC) system in high-grade office buildings for energy saving, good heat comfort, flexible control and most adapting to space with variable load conditions. However, VAV air-conditioning systems tend to have more faults due to the complexity of VAV air-conditioning systems and higher requirements of their control systems. Faults will result in the increase of energy consumption, the deterioration of heat comfort, the damage of components and the increase of maintain costs. Energy management and control systems (EMCS) are widely employed in modern buildings. The huge amount of data available on EMCS systems provides rich information for online fault diagnosis of HVAC systems. Online fault detection and diagnosis for HVAC systems has already received increasing attention recently.Therefore, research on online fault detection and diagnosis (FDD) of VAV air-conditioning systems is of great importance in economy and engineering. Online fault detection and diagnosis methods for VAV air-conditioning systems are deeply investigated in this study from the perspective of main components.
The results of model-based FDD are strongly dependent on the accuracy of HVAC component models. The accuracy of HVAC models can be improved if model parameters are tuned by using the collected operating data of HVAC systems. To this end, component models with self-tuning parameters for air handling units are presented in this study. Model parameters are tuned by using a genetic algorithm (GA) which minimizes the error between measured and estimated performance data. The propsed models were validated against real data gathered from existing HVAC systems. The validation results show that component models with self-tuning parameters have higher prediction precisions and can be used to detect faults in air handling units. An online adaptive scheme is also developed to update the fault detection thresholds, which vary with system operating conditions. Fault detection thresholds are determined by using a statistical method. Adaptive fault detection thresholds can improve the accuracy of fault detection and facilitate the practical implementations of model-based FDD methods.
As for fault detection and diagnosis of VAV air handling units, each single method is flawed and ineffective in real implementations. An online fault detection and diagnosis strategy for VAV air handling units is presented based on component models with self-tuning parameters and expert rules. Component models with self-tuning parameters are used to detect faults in air handling units. Three rule-based fault classifiers are developed to find fault sources. The proposed fault detection and diagnosis strategy was online validated and tested on real VAV air-conditioning systems.
Proper design parameters of CUSUM control chart are very essential to design CUSUM control charts. Improper parameter values will deteriorate the performances of CUSUM control charts in practice. To this end, this study introduces the methods by which the essential design parameters of CUSUM control chart and the required parameters of the control process can be identified. Mathematical analysis method is used to analyze the effect of serial correlation on the performance of control charts using data from the first order autoregressive process (AR(1)) and the first order moving average process(MA(1)). Based on the above study, residual-based CUSUM control charts are used to detect faults in VAV terminals. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes.
An online fault detection and diagnosis strategy for VAV terminals is presented based on residual-based CUSUM control charts and expert rules. Residual-based CUSUM control charts are used to detect faults in VAV terminals. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to find fault sources. The proposed fault detection and diagnosis strategy for VAV terminals was online validated and tested on real VAV air-conditioning systems.
Based on theoretical study, an online fault detection and diagnosis software for VAV air-conditioning systems is developed, which consists of an online fault detection and diagnosis software for VAV air handling units and an online fault detection and diagnosis software for VAV terminals. The new-developed FDD softwares were online implemented and validated on real VAV air-conditioning systems. The validation results show that the new-developed FDD softwares can depict operational condition of HVAC equipments and find fault sources precisely.
引文
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