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.     

Bayesian network based FDD strategy for variable air volume terminals

This paper presents a diagnostic Bayesian network (DBN) for fault detection and diagnosis (FDD) of variable air volume (VAV) terminals. The structure of the DBN illustrates qualitatively the casual relationships between faults and symptoms. The parameters of the DBN describe quantitatively the probabilistic dependences between faults and evidence. The inputs of the DBN are the evidences which can be obtained from measurements in building management systems (BMSs) and manual tests. The outputs are the probabilities of faults concerned. Two rules are adopted to isolate the fault on the basis of the fault probabilities to improve the robustness of the method. Compared with conventional rule-based FDD methods, the proposed method can work well with uncertain and incomplete information, because the faults are reported with probabilities rather than in the Boolean format. Evaluations are made on a dynamic simulator of a VAV air-conditioning system serving an office space using TRNSYS. The results show that it can correctly diagnose ten typical VAV terminal faults.     

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 study on the energy penalty of various air-side system faults in buildings

Automatic fault detection and diagnosis (FDD) can help enhance building energy efficiency by facilitating early detection of occurrence of system faults, especially those of air-conditioning systems, thus enabling rectification of the faults before much energy is wasted due to such faults. However, building owners may not invest in FDD unless they are convinced of the energy cost savings that can be achieved. This paper presents the results of a study on the energy cost impacts of a range of common system faults in variable air volume (VAV) air-conditioning systems, which are widely adopted for their good part-load energy efficiency. The faults studied include room air temperature sensor offset, stuck VAV box damper, supply air temperature sensor offset, stuck outdoor air damper and stuck/leaking cooling coil valve. The simulation results indicate that some faults may significantly increase energy use in buildings, for example, negative room air temperature sensor offset, stuck open VAV box damper, negative supply air temperature sensor offset, stuck open outdoor air damper and stuck open and leaking cooling coil valve. Since building occupants may adapt to the symptoms of these faults, such as reduced room air temperature, and thus may not complain about them, the occurrence of such faults are not immediately apparent unless a FDD system is available. Some other faults, e.g. positive supply air temperature sensor offset, positive room air temperature sensor offset, stuck closed cooling coil valve and stuck closed VAV box damper, may allow less energy to be used but will lead to unbearable indoor environmental conditions, such as high indoor temperature. Such faults, therefore, can easily be detected even without a FDD system, as there will be feedback from the building occupants.     

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.     

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.     

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。     

Fault detection and diagnosis based on improved PCA with JAA method in VAV systems

In this paper, improved principal component analysis (PCA) with joint angle analysis (JAA) is presented to detect and diagnose both fixed and drifting biases of sensors in variable air volume (VAV) systems. Fault characteristic concerned in PID controller in the VAV systems is analyzed and discussed. The squared prediction error (SPE) plot based on PCA is used to detect the sensor fixed and drifting biases. Then the JAA plot instead of conventional contribution plot is used to diagnose the faults. And they are tested and evaluated online in a simulated centralized VAV air-conditioning systems.     

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.     

螺杆式冷水机组的故障模拟及诊断软件的研究

中央空调系统故障排除通常中凭经验判断,这会造成一定的滞后甚至差错。洁净空调类的中央空调系统发生故障时,往往会带来更大的经济损失。因此,HVAC设备与系统故障诊断技术的研究和应用,对控制修复、故障排除和故障预防都有十分重要的意义。本文针对制冷机的工作特性和螺杆冷水机组故障进行了理论分析,得出了螺杆式冷水机组常见故障的表现特征,并在自行设计的实验系统上模拟了8个螺杆式冷水机组的常见故障。通过对实验数据的分析,筛选了5个制冷机运行参数作为故障判断的特征参数,通过分析研究这几个参数的变化规律总结出故障判断准则,在此基础上,编写了一套故障诊断软件,并在该实验系统上进行了 验证,结果表明该故障诊断软件运行可靠、诊断故障准确。     

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.     

Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers

Heating, ventilation, air-conditioning, and refrigeration (HVAC&R) systems operated under faulty condition often result in extra energy consumption (up to 30% for commercial buildings) and cost, less comfort control and bad indoor/outdoor air quality, especially when multiple faults happening simultaneously. This study presents a novel hybrid strategy that combines support vector machine (SVM) and multi-label (ML) technique for the automated detection and diagnosis of multiple-simultaneous faults (MSF), and elaborates its application to a building chiller. One of the great advantages ML has against the mono-label (mL) technique is that no MSF data are needed for model training while a good FDD performance for MSF could be obtained. Two individual chiller faults and one of their combinations (an MSF) were investigated. Detailed studies on the use of three features sets and the training of the model with/without normal or/and MSF data were conducted and compared with the mL-SVM model. The results show that the ML-SVM model trained on the normal and two individual faults has an excellent performance, especially when the eight fault-indicative features (Feat8) were employed (correct rate over 99.9%). Feat8 behaves still excellent even when Gaussian white noise has been added to the test data.     

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.     

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.     

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 robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals

A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals is presented to ensure well capacity of energy conservation in building air conditioning systems. Principal component analysis (PCA) is used to detect the sensor faults including fixed bias, drifting bias and complete failure. To improve the detection efficiency, several PCA models are built through employing the conservation equations and control relations of the system. With the historical data, PCA models are trained to capture most useful information of normal operation. As a result, the training models can identify whether the present condition is abnormal through comparing the residues with the thresholds. Since the principal component subspace and residue subspace of the operation data space are obtained using PCA decomposition, these two subspaces are used to develop the fault isolation scheme. The new fault detected and the known ones in the library are all projected into the principal component subspace and residue subspace decomposed by PCA. The joint angle plot, illustrating the direction relations of the projections in both subspaces between the new fault and the known ones, is used to diagnose the fault source.     

用小波分析法分离变风量系统流量传感器故障

阐述了主成分分析法对变风量（VAV）系统流量传感器故障识别失效的原因,给出了小波分析法的特点及其原理,着重描述了如何运用小波分析分离VAV系统中的共线性流量传感器故障。实例研究结果表明：小波分析能够很好地识别共线性的流量传感器故障。