真实建筑中变风量空调末端装置故障在线检测与诊断研究

本文针对真实建筑中变风量空调末端装置故障进行了研究,开发了一个在线检测与诊断变风量空调末端装置故障的工具。该故障诊断工具采用一个基于混合方法的故障检测与诊断策略。累积和控制图被用于累加房间温度测量值及其设定值之间的偏差,如果计算的累积和大于控制图上限值,或者小于控制图下限值,就意味着在变风量空调末端装置中存在故障或者异常。基于规则的故障分类器被用于分离和诊断故障源。故障诊断工具已经在香港某大型办公建筑进行了在线应用和验证,验证结果表明该故障诊断工具可以准确有效地检测和诊断变风量空调末端装置故障。     

建筑能源管理与控制系统中传感器故障及其检测与诊断

描述了传感器故障类型，给出了其故障函数。用主成分分析法对建筑能源管理与控制系统测量数据进行建模并对空调检测系统中的四类传感器故障进行检测与诊断。结果表明主成分分析法具有很好的故障检测和故障诊断能力。     

TBM故障诊断专家系统的设计与开发

基于专家系统工具，开发了TBM状态监测和故障诊断专家系统，阐述了系统的结构原理、推理规则、目标实现过程。系统集监测、数据库管理、故障诊断推理、检修方案等功能于一体，能预报TB880E型掘进机渐进形故障，对突发形的故障能迅速诊断故障部位，可缩短故障诊断时问，提高设备的完好率及利用率。     

风电齿轮箱故障诊断信息集成系统研究与开发

以风电机组增速齿轮箱故障为对象,基于信号检测与处理技术、网络和数据库集成开发技术的应用,以及基于增速齿轮箱的结构和运行故障特征分析与研究,开发了基于网络环境下的风力发电机组增速齿轮箱故障诊断信息系统。从总体上设计该信息系统物理结构,详细阐述了软件系统的结构和各个子系统的功能;最后给出了故障诊断信息系统的原型系统。该信息系统的研究与原型系统的开发,对研究大型并网机组远程故障诊断、提高诊断信息集成度和机组运行水平,都具有较好的应用价值。     

工程机械野外修理车

文章介绍了工程机械野外修理车的总体构成,主要由运载车、检测维修方舱、自备发动机组、检测诊断仪器、维修设备、维修工具等组成。并阐述了工程机械发动机综合检测系统、液压系统的介入式和非介入式故障检测系统、多功能液压工作站等重点设备的工作原理。     

用参数自整定模型在线检测空气处理机组故障

大型现代建筑大都安装了能源管理与控制系统(EMCS),EMCS系统储存的大量监控数据为空调系统的在线故障检测与诊断提供了方便。提出了一种利用参数自整定空调部件模型在线检测变风量空气处理机组故障的方法。利用遗传算法优化模型参数使模型预测数据与实测值数据的残差最小,因此空调部件模型有较高的预测精度。若模型预测数据与实测数据的残差超出了预先设定的阈值,就意味着变风量空气处理机组可能存在故障。针对在实际应用时确定故障检测阈值的困难,给出了用统计方法确定阈值的方法。故障检测方法在真实建筑中进行了应用和验证,结果表明该故障检测方法可以结合EMCS系统准确有效的检测变风量空气处理机组故障。     

暖通空调系统故障检测与诊断研究进展

介绍了暖通空调系统发生故障的原因及常见故障,比较了几种常用的故障检测与诊断方法的优缺点.介绍了国内外暖通空调故障检测与诊断的研究现状和应用情况.展望了发展方向.     

自动故障检测与诊断在暖通空调中的研究与应用

介绍了暖通空调系统中自动故障检测与诊断的基本流程及故障分类。回顾了暖通空调行业中自动故障检测与诊断的研究现状、常用方法及应用情况。分析了故障诊断所面临的困难及今后的研究方向。     

避雷器的常见故障及红外诊断

对运行中的避雷器进行在线红外诊断是电力设备带电诊断行之有效的技术手段。分析了FS、FZ、FCZ、FCD型避雷器和金属氧化物避雷器在正常状态和故障状态的发热特点,认为避雷器故障主要是受潮引起的,热像特征主要表现为局部温升。用红外热像仪对运行中的避雷器进行红外测温和故障分析,结果表明,应用红外诊断技术检测避雷器故障,可靠性较高。     

暖通空调系统智能化故障检测诊断研究综述与展望

暖通空调系统智能化化故障检测与诊断对提高运维水平和能源效率具有重要意义。本文总结了暖通空调故障检测与诊断领域近二十多年来的研究历程,探讨了基于规则、基于模型和基于数据等三类主流方法的优劣,深入剖析了暖通空调系统故障检测与诊断研究中的关键问题,并对未来该领域的发展方向进行了展望。     

基于HVAC信息流的故障检测与诊断系统研究

介绍了一种新的HVAC故障检测与诊断系统，该系统利用人工神经网络技术处理信息流，在信息流分析的基础上，采用面向对象的编程思想，和基于模型的FDD方法相结合，最后描述了该故障检测与诊断系统的实现过程。     

A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems

There are many reports about faulty status in building air-conditioning systems recently. It becomes difficult to keep indoor air temperature appropriately as faults occur, and the faults cause waste of building energy consumption. The model-based fault detection and diagnosis (FDD) methods have been researched for specific parts of air-conditioning system such as chillers, coils, variable air volume units (VAV units), etc. It needs, however, much time and labor to monitor and check every single part because we cannot predict where and when the faults occur. The purpose of this study is to examine indoor air temperature changes and energy consumption increase when faults occur and to develop an easy-to-use FDD tool that helps to find out the faulty place through the whole building air-conditioning system. And then, we treat the reliability of the proposed FDD tool and effectiveness to control of indoor environment deterioration and energy consumption increase by the tool is evaluated based on building air-conditioning system simulation in this paper.     

A diagnostic tool for online sensor health monitoring in air-conditioning systems

Healthy sensors are essential for the reliable monitoring and control of building automation systems (BAS). This paper presents a diagnostic tool to be used to assist building automation systems for online sensor heath monitoring and fault diagnosis of air-handling units. The tool employs a robust sensor fault detection and diagnosis (FDD) strategy based on the Principal Component Analysis (PCA) method. Two PCA models are built corresponding to the heat balance and pressure-flow balance of an air-handling process. Sensor faults are detected using the Q-statistic and diagnosed using an isolation-enhanced PCA method that combines the Q-contribution plot and knowledge-based analysis. The PCA models are updated using a condition-based adaptive scheme to follow the normal shifts in the process due to changing operating conditions. The sensor FDD strategy, the implementation of the diagnostic tool and experimental results in an existing building are presented in this paper.     

A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults

This paper presents a strategy for fault detection and diagnosis (FDD) of HVAC systems involving sensor faults at the system level. Two schemes are involved in the system-level FDD strategy, i.e. system FDD scheme and sensor fault detection, diagnosis and estimation (FDD&E) scheme. In the system FDD scheme, one or more performance indices (PIs) are introduced to indicate the performance status (normal or faulty) of each system. Regression models are used as the benchmarks to validate the PIs computed from the actual measurements. The reliability of the system FDD is affected by the health of sensor measurements. A method based on principal component analysis (PCA) is used to detect and diagnose the sensor bias and to correct the sensor bias prior to the use of the system FDD scheme. Two interaction analyses are conducted. One is the impact of system faults on sensor FDD&E. The other is the impact of corrected sensor faults on the system FDD. It is found that the sensor FDD&E method can work well in identifying biased sensors and recovering biases even if system faults coexist, and the system FDD method is effective in diagnosing the system-level faults using processed measurements by the sensor FDD&E.     

冷凝器结垢故障对传感器故障诊断方法的影响分析

传感器测量质量是实施系统监控，优化控制及部件故障诊断的重要保证。制冷机系统的部件故障和传感器故障很有可能同时发生。因而在研究传感器故障诊断的方法及应用这一方法进行传感器故障诊断时必须考虑到制冷机的部件故障的影响。根据离心式制冷机的试验数据研究了冷凝器结垢故障对基于主元分析法的制冷机传感器故障诊断方法的影响，研究结果表明基于主元分析的制冷机传感器方法对冷凝器结垢故障不敏感，该方法对有无结垢故障条件下的传感器故障都能成功地进行检测诊断及数据重构。     

基于主成分分析法的空调系统传感器自动故障诊断

由于各种各样的故障,空调系统在整个建筑生命周期内其性能很少能够达到设计目标。为了改善空调系统的性能,在整个建筑生命周期内对其进行定期或连续检测是很必要的。随着空调系统的日益复杂化和对快速可靠检测的需求,人工检测已远远不能满足要求,这使自动检测成为了必须。目前国际上研究建筑空调系统自动检测的技术很热门,但较少考虑空调系统传感器的准确性。传感器读数准确性是空调系统可靠控制和检测的先决条件。本文提出了一种基于主成分分析法的鲁棒故障诊断策略,该策略可以有效地对空气处理单元中的传感器进行自动检测。结合建筑自动化系统,该诊断策略可以实现空调系统传感器故障的在线检测。     

Online fault detection and diagnosis in VAV air handling unit by RARX modeling

Assimilation of cost-effective fault detection and diagnosis (FDD) technique in building management system can save enormous amount of energy and material. In this paper, recursive autoregressive exogenous algorithm is used to develop dynamic FDD model for variable air volume (VAV) air handling units. A methodology, based upon frequency response of the model is evolved for automatic fault detection and diagnosis. Results are validated with data obtained from a real building after introducing artificial faults. Robustness of the method is further established against sensor errors arising out of faulty bias during long term use or lack of proper commissioning. It is concluded that the method is quite robust and can detect and diagnose several types of faults. A short and simple method is also included in this paper to detect the faults of VAV units operating in the same zone by comparing their behavior. The new method, which requires very small amount of computation time, was tested with the aforementioned database and shows satisfactory results.     

基于BAS的空调系统过程监测与故障诊断

提出了基于BAS的空调系统过程监测与故障诊断模式，阐述用主成分分析法进行故障诊断的过程，并用其进行空调系统的传感器故障诊断。结果表明，主成分分析法具有很好的故障检测、故障识别和故障重构能力，还表明，故障诊断与BAS相结合是可行的。     

A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances

This paper presents the results of a site survey study on the faults in variable air volume (VAV) terminals and an automatic fault detection and diagnosis (FDD) strategy for VAV air-conditioning systems using a hybrid approach. The site survey study was conducted in a commercial building. 20.9% VAV terminals were ineffective and 10 main faults were identified in the VAV air-conditioning systems. The FDD strategy adopts a hybrid approach utilizing expert rules, performance indexes and statistical process control models to address these faults. Supported by a pattern recognition method, expert rules and performance indexes based on system physical characteristics are adopted to detect 9 of the 10 faults. Two pattern recognition indexes are introduced for fault isolation to overcome the difficulty in differentiating damper sticking and hysteresis from improper controller tuning. A principal component analysis (PCA)-based method is developed to detect VAV terminal flow sensor biases and to reconstruct the faulty sensors. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in situ tests.     

变流量冷媒水系统温度及流量传感器故障诊断

本文针对中央空调冷媒水侧的6个流量计和8个温度传感器，提出了一种基于数理统计的故障诊断方法。在变水量动态仿真器上，对选取的控制体建立守恒方程，根据测量的温度、流量值，运用守恒关系的残差表达式判断传感器是否有故障，再根据残差表达式的统计数学特性建立了诊断方程组，通过求解诊断方程组，得到传感器的固定偏差。