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。     

基于EWMA控制图和规则的变风量空调箱故障诊断

针对变风量空调箱故障,考虑了数据自相关性、系统动态性和瞬变过程等因素的不利影响,本文给出了1种基于EWMA控制图和专家规则的变风量空调箱故障诊断方法。残差EWMA控制图被用于检测变风量空调箱故障,该方法既可以消除空调负荷突变、运行参数改变、开机等瞬变过程和系统动态性的不利影响,又可以消除数据自相关性对控制图性能的不利影响,可以提高故障检测的准确性。设计了基于规则的故障分类器,可分离和寻找故障源。故障诊断方法在实际系统中进行了应用和验证,验证结果表明该故障诊断方法具有较好的鲁棒性和较高的准确率。     

解析暖通空调工程机电系统安装施工技术

机电安装施工在建筑工程中是很关键的环节,机电安装的质量直接影响了整个建筑性能的发挥。暖通空调安装施工技术是建筑机电安装中必不可少的环节,对空调系统采用先进的技术安装处理,决定了建筑室内生活和工作环境的优越性。为此,本文从暖通空调在机电安装在施工中存在的问题和节能技术与优化机电安装进行分析,针对暖通空调安装施工技术在机电安装中的综合策略进行探讨。     

暖通空调的自动故障检测及诊断分析

暖通空调技术是现代建筑中应用较为广泛的一种新型空调技术,其作用在于为建筑物内部提供一个合理的建筑温度环境。但是这一类的空调系统因消耗功率较大,且易产生故障,需要进行定期的检测诊断分析,并根据其所呈现出来的问题拟定相应的维修护理,从而确保建筑物内的操作环境,并完善其整体的有效性管理调控。伴随着现代社会的发展你,应用自动化故障检测装置进行故障检测,已成为了发展的主要目的。本文针对暖通空调的自动故障检测诊断技术进行简要分析。     

离心式制冷机系统传感器故障诊断的试验研究

传感器的可靠性及准确性对制冷机系统的可靠控制和系统的最优运行起着至关重要的作用。同时,传感器的读数也是进行部件故障诊断的基础。本文提出了基于主元分析法的制冷机传感器故障诊断方法,该方法的主元分析模型由离心式制冷机系统中的相关测量变量在正常条件下的观测样本构成。这一方法利用这些变量在正常条件下的相关性来对传感器的测量观测值进行故障检测与诊断及测量重构,并分别用Q-统计及Q-分布图来对传感器故障进行检测及诊断。本文利用实验室离心式制冷机的试验数据对这一基于主元分析法的传感器故障诊断方法进行了验证。     

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。     

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。     

自动化控制系统在河南电视台办公大楼空调系统改造中的应用

空调耗能是建筑物耗能中的大户,空调自控系统可以使建筑内环境更舒适、设备运行更可靠、建筑能耗更少,是现代楼宇空调系统重要的组成部分。     

基于机房设备表具图像的非侵入式故障诊断算法

空调系统的故障诊断是维持空调系统正常运行、降低建筑能耗、提升空调系统设备使用寿命的重要手段。其中,冷冻机房内的设备和系统造价及耗能占比较高,因此冷冻机房内设备和系统的故障诊断是空调系统故障诊断中至关重要的一部分。现有的针对冷冻机房内设备和系统的故障诊断研究大多依赖于建筑自动系统(BA)采集的传感器数据,传感器在实际应用中常因自身损坏和通信故障导致BA系统无法获取准确信息,而大部分大型设备中自带且更为可靠的机械表数据却没有被纳入到自动监测中。利用基于图像的故障诊断算法,以故障状态监测为目的,以冷冻机房比较普遍的日常巡检任务为切入点,以实现利用能够采集图像的巡检机器人对冷冻机房的主要设备进行自动巡检,达到空调系统的智能化管理为愿景,开发多种自动化的故障诊断算法模块。这些诊断模块具有非侵入、成本低、易部署等优点。基于图像的算法进行温度和压力表盘的示数读取。先利用迁移学习,关闭特征层的梯度更新的方式训练一个AlexNet卷积神经网络,对冷冻机房中的表盘、卧式水泵、立式水泵、压缩机4种主要设备进行分类。对于温度和压力表盘,利用图像二值化、最大连通域提取、形态学处理和边缘检测等多种图像处理方法获取...     

某超高建筑空调系统排风全热回收方案的生命周期成本分析

随着工业化及城镇化进程的快速发展,建筑能耗呈逐年上升趋势,尤其是空调能耗已经成为当前建筑中耗能最大的组成部分之一。现以深圳某超高层办公大楼为例,对其排风热回收的生命周期成本和回收周期进行分析,对在调系统中,应用排风全热热回收装置,系统负荷及能耗的影响进行了分析。在此基础上利用生命周期成本分析方法得出其经济性能,指出该技术具有广阔的应用前景,特别适合南方潮湿地区。     

AHU sensor fault diagnosis using principal component analysis method

The paper presents a strategy based on the principal component analysis (PCA) method, which is developed to detect and diagnose the sensor faults in typical air-handling units. Sensor faults are detected using the Q-statistic or squared prediction error (SPE). They are isolated using the SPE and Q-contribution plot supplemented by a few simple expert rules. Two PCA models are built based on the heat balance and pressure–flow balance of the air-handling process, aiming at reducing the effects of the system non-linearity and enhancing the robustness of the strategy in different control modes. The fault isolation ability of the method is improved using the multiple models. Simulation tests and site data from the building management system (BMS) of a building are used to verify the PCA-based strategy for automatic validation of AHU monitoring instrumentations and detecting/isolating AHU sensor faults under typical operating conditions. The robustness of the PCA-based strategy in detecting/diagnosing AHU sensor faults is verified. Effects of sensor faults and the strategy energy efficiency of an automated AHU are evaluated using simulation tests.     

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 hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis

This paper presents a self-adaptive sensor fault detection and diagnosis (FDD) strategy for local system of air handing unit (AHU). This hybrid strategy consists of two stages. In the first stage, a fault detection model for the AHU control loop including two back-propagation neural network (BPNN) models is developed. BPNN models are trained by the normal operating data of system. Based on sensitive analysis for the first BPNN model, the second BPNN model is constructed in the same control loop. In the second stage, a fault diagnosis model is developed which combines wavelet analysis method with Elman neural network. The wavelet analysis is employed to process the measurement data by extracting the approximation coefficients of sensor measurement data. The Elman neural network is used to identify sensor faults. A new approach for increasing adaptability of sensor fault diagnosis is presented. This approach gains clustering information of the approximations coefficients by fuzzy c-means (FCM) algorithm. Based on cluster information of the approximation coefficients, the unknown sensor fault can be identified in the control loop. Simulation results in this paper show that this strategy can successfully detect and diagnose fixed biases and drifting fault of sensors for the local system of AHU.     

全寿命周期评价下建筑材料资源的可持续设计策略

对建筑业而言,寿命周期评价不仅是一种评估工具,更是一种设计思想和设计方法。该文简要介绍了寿命周期理论,对建筑系统寿命周期内的物质体系和功能体系进行综合评价和分析,指出保持建筑材料可持续利用和提高建筑资源综合利用率是可持续建筑设计的必然选择,并总结探讨了建筑材料可持续利用的相关设计策略。     

A rule-based fault detection method for air handling units

Air handling unit performance assessment rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units (AHUs). Control signals are used to determine the mode of operation of the AHU. A subset of the expert rules which correspond to that mode of operation are then evaluated to determine whether a fault exists. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon the sensor data and control signals that are commonly available in these systems. APAR was tested using data sets collected from a “hardware-in-the-loop” emulator and from several field sites. APAR was also embedded in commercial AHU controllers and tested in the emulator.     

Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS on-line control strategies

Dynamic models are developed to simulate the thermal, hydraulic, environmental and mechanic characteristics and energy performance of a building and VAV air-conditioning system under the control of EMCS. Three on-line supervisory strategies and programs based on integrated EMCS stations are developed to optimise the VAV static pressure set-point, AHU outlet air temperature set-point and outdoor ventilation air flow set-point, The strategies and programs are commissioned and evaluated under the simulated ‘real-life’ environment. This paper presents the dynamic models, the control strategies and the simulation exercises for commissioning and evaluation of the strategies.     

A sensor fault detection strategy for air handling units using cluster analysis

Sensors are an essential component in the control systems of air handling units (AHUs). A biased sensor reading could result in inappropriate control and thereby increased energy consumption or unsatisfied indoor thermal comfort. This paper presents an unsupervised learning based strategy using cluster analysis for AHU sensor fault detection. The historical data recorded from sensors is first pre-processed to reduce the dimensions using principal component analysis (PCA). The clustering algorithm Ordering Points to Identify the Clustering Structure (OPTICS) is then employed to identify the spatial separated data groups (i.e. clusters), which possibly indicate the occurrence of sensor faults. The data points in different clusters are then checked for temporal separation in order to confirm the occurrence of sensor faults. The proposed sensor fault detection strategy is tested and evaluated with the data collected from a simulation system. The results showed that this strategy can detect single and non-simultaneously occurred multiple sensor faults in AHUs. The fault detection results were not strongly affected by the selection of the user defined input parameters required in OPTICS.     

基于全生命周期的公共建筑节能净收益测算模型建立

对公共建筑节能激励机制参与主体经济性分析是解决激励相容问题的重要内容。运用全生命周期理论和生命周期成本方法,对公共建筑节能全过程进行阶段划分,剖析公共建筑节能全生命周期过程所产生的各项增量成本和所获得的各项增量效益;针对中央政府、地方政府、业主和节能服务公司等公共建筑节能主要参与主体,建立净收益测算模型,包括测算公式和定量测算方法。该模型既可以作为政府确定公共建筑节能激励强度的计算依据,又可以作为公共建筑业主和节能服务公司节能行为的决策分析工具。     

建筑材料生命周期中环境状况的评价与研究

建筑材料是构建建筑物的物质基础,其环境影响是建筑物生命周期环境影响的一个重要组成部分。通过生命周期评价方法评价建筑材料在预建构阶段、建设阶段、后建设阶段三部分产生的环境影响。积极推行建筑材料的再生循环,贯彻资源与环境可持续发展的战略决策。     

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.