Application of pattern matching method for detecting faults in air handling unit system

This paper presents a hybrid air handling unit (AHU) fault detection strategy based on Principal Component Analysis (PCA) method and Pattern Matching method. The basic idea of the pattern matching method is to locate periods of operation from a historical data set whose operational conditions are similar to the target operating condition. The proposed Pattern Matching-PCA method uses two similarity factors, PCA similarity factors and Distance similarity factors, to characterize the degree of similarity between historical data window and current snapshot data. PCA model is then built using the historical AHU operation dataset that are identified to be similar to current snapshot operation data. The method is validated by operational data of an AHU system in real building. The results show that the sensibility of PCA models is enhanced by preprocessing the training data with the Pattern Matching method.     

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 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.     

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.     

Numerical investigation on the ingress of particulate matter from ambient air into the inlet of a building air handling unit

The transportation of ambient particulate matter (PM) from outdoor air into the inlet of a mechanical building ventilation system is poorly understood. No studies have examined the effect commonly used commercial air handling unit (AHU) inlet designs have upon the migration of PM from the ambient environment into the building ventilation system, and implications of this on energy consumption and indoor air quality (IAQ). Through the numerical analysis of commercial AHU inlets, the differences in concentration of PM in ambient air and that within AHUs were determined, more commonly referred to as Aspiration Efficiency (AE %). A 20–50% difference in particle concentrations between ambient air and the in-AHU concentration was observed between forward and rear-facing AHUs relative to ambient wind direction and speed, and at the maximum ventilation flow rate. Furthermore, a decrease in the ventilation flow rates resulted in a significant reduction in PM concentrations entering the rear-facing AHU. Increasing the Stoke number led to lower AE as a continuous decrease was observed for both rear-facing inlets. The findings of this paper show that AHU inlet design has significant implications on IAQ and building energy consumption, and scope exists to design these inlets to impact both aspects positively.     

A novel model-based fault detection method for temperature sensor using fractal correlation dimension

The direct residual-based fault detection method compares the difference between measured and estimated data of a process variable. Its correct fault detection rate is low due to the noise in measured signals. A novel method using fractal correlation dimension (FCD) is developed, in which FCD deviation is adopted instead of direct residual. The method is validated by detecting fixed and drifting bias faults generated in supply air temperature sensor of air handling unit (AHU) system. The setting of three main parameters including embedding dimension, time delay parameter and length scale, is investigated to find out the influence on calculating FCD values. The results show that it is more efficient to detect relatively small bias fault under noise conditions, although it needs a period of time to collect measured data. As a promising and practical tool, a hybrid fault detection technique combining FCD with direct residual should be conducted in further investigation to identify the generated fault under inevitable noise conditions.     

基于专家规则的空调系统在线故障检测与诊断

本文针对一次回风空调系统过渡季全新风经济运行模式中的易发性故障进行了研究,提出了故障检测与诊断的专家规则集,并采用HVACSim+软件分别模拟了空调系统联动风阀卡在非全新风状态、冷冻水供水温度远高于设计值、冷冻水阀卡在大或小开度处以及新风传感器偏差过大5种故障下的运行状态。模拟结果验证了规则集的正确性,表明基于该规则集的故障诊断方法可有效用于空调运行的实时监测和故障诊断,有助于系统运行的优化。     

CONFAULT-AN EXPERT SYSTEM FOR FAULT DIAGNOSIS IN REINFORCED CONCRETE STRUCTURES

Diagnostic applications are especially suitable for expert systems. The expert system CONFAULT diagnoses faults in reinforced concrete structures by identifying fault sub types. The knowledge base in CONFAULT is divided into modules corresponding to six major fault types, while meta rules are used to control and limit searching. A modified confidence factor approach is used to deal with uncertainty.     

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。     

一次回风空调箱冷却除湿过程计算

分析了舒适性空调系统中由空调箱选型不当引起的空气处理偏离设计状态点的现象。验证了风机盘管表冷器修正公式应用于空调箱的可行性。提出了空调箱特性指标(S0B00.162)的概念,建立了一次回风系统空调箱空气热湿处理过程数学模型,并使用MATLAB软件编程求解模型,在一定设计条件下计算出空调箱的出风参数、供回水温差,并以供回水温差作为空调箱选型的依据。运用模型对两个气候差异较大地区的候车厅空调箱进行选型计算。建立的数学模型及求解方法同时可作为空调箱运行调节的依据。     

A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings

A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. 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. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems.     

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。     

某大型空调系统能耗分析

能源问题是决定我国能否可持续发展的关键因素之一。对某大型空调系统能耗进行了测试和分析，表明冷机实际COP低于其额定COP，并且空调箱能耗占到30％-50％；另外，对系统的负荷分析表明，造成系统的供需比下降的主要原因是该系统设计本身所造成。因此，对空调系统优化运行是节能改造必不可少的一个环节；同时，如何优化空调系统各级的设定参数以及系统的划分，将会从根本上提高系统冷负荷的供需比，从而大大减少系统能耗，可为大型空调系统的运行人员、设计人员提供参考。     

Online fault detection and robust control of condenser cooling water systems in building central chiller plants

This paper presents a robust strategy for online fault detection and optimal control of condenser cooling water systems. The optimal control strategy is formulated using a model-based approach, in which simplified models and a hybrid quick search (HQS) method are used to optimize the performance of the overall system by changing the settings of the local process controllers. A system level online fault detection scheme is embedded into the control system and used to monitor whether the system operates in a healthy condition. The faults considered are mainly the component performance degradations. When a fault is detected, the control system will be reconstructed to regain the control through using robust schemes. The performance of the proposed strategy is tested and evaluated against on a simulated virtual system representing the actual condenser cooling water system in a super high-rise building. The results show that the fault detection scheme is effective in identifying system performance degradations and the fault-tolerant control strategy with online fault detection and optimal control can enhance the overall system performance significantly when the operation of condenser cooling water systems suffers from performance degradations, as compared to that using optimal control only.     

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.     

Use of genetic algorithms to develop an adaptive fuzzy logic controller for a cooling coil

An adaptive approach to control a water valve for a cooling coil, called an adaptive fuzzy logic controller (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and the supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and continues to improve the fuzzy controller parameters to minimize the error. The AFLC uses genetic algorithms (GAs) to improve the fuzzy rule matrix and fuzzy membership functions for the AHU in HVAC systems. In this paper, the application of genetic algorithms for developing the AFLC is presented. After a brief background on fuzzy logic controllers and GA theory, the use of GAs is explained. Three methods of modifying the fuzzy rule matrix using the GAs are presented along with simulation and real-time experimental results. Experimental results indicate that GAs can be successfully applied to modify an AFLC rule matrix to achieve a better controller.     

Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units

Heating Ventilation and Air Conditioning (HVAC) system energy consumption on average accounts for 40% of an industrial sites total energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning Air Handling Units (AHUs) in HVAC systems to rectify faulty operation. Studies have also demonstrated that on-going commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems. AFDD can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHUs using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHUs across four commercial & industrial sites with over €104,000 annual energy savings detected by the AFDD tool.     

住宅夏季空调行为对空调能耗的影响研究

本文针对北京地区的城镇住宅,采用问卷调查的方式,从居住者使用的角度分析了住宅空调行为在空调设定温度、空调运行时间、空调同时运行台数及空调运行时门窗开关状况等方面的分布,研究发现了住宅空调行为的变化特点以及不同住户空调行为模式的差异。在此基础上,采用模拟分析的方法,计算确定了空调行为模式对住宅空调能耗的影响程度。最后,对空调实际耗电量与模拟计算结果进行了比较,验证了空调行为调查统计结果的可靠性。     

建筑空调系统诊断研究进展

中央空调系统是商业建筑的能耗大户,对中央空调系统的监控有利于掌握系统是否正常运行。当系统不能正常运行时,及时对系统进行诊断,并找出问题所在并提出解决问题的办法;即使系统正常运行,及时对系统的各种指标进行评估以判断系统是否在高能效下运行。因此对建筑空调系统进行各种故障的检测及诊断对保证系统的正常运行,提高系统能效及保证室内环境有重要意义。本文回顾了建筑空调系统故障诊断的研究情况及进展,同时指出这方面的研究应该加强各种诊断策略及方法在实际工程应用方面的研究。     

Development and validation of online models with parameter estimation for a building zone with VAV system

The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked increasing attention to promote energy efficient control and operation of HVAC systems. Application of advanced control and operation strategies requires robust online system models. In this study, online models with parameter estimation for a building zone with a variable air volume system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and zone entering air flow are modeled based on physical rules and only the measurements that are commonly available in a commercial building are used. Various validation experiments were performed using a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all validation experiments are less than 0.28 °C for temperature outputs, and less than 84.9 m³/h for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.