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 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。     

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 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.     

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.     

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 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.     

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.     

变风量控制系统稳定性初探

控制系统产生振荡是VAV空调系统的最大问题之一，建立了空调室及控制器的动态特性模型，开发了计算机仿真系统，并对VAV空调系统进行了仿真。仿真结果表明控制系统的稳定范围受控制参数和系统参数的影响。得到的极限参数ku,εu的数学式对实践有一定指导作用。     

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。     

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.     

基于主成分法的VAV系统传感器故障检测与诊断

众所周知，VAV系统对控制的要求很高，作为控制系统中非常关键的元件——传感器，一旦出现故障将直接影响控制系统的决策，从而使VAV系统的运行偏离设计要求。因此，VAV系统传感器的故障检测与诊断研究是很有必要的。本文采用主成分分析法(PCA，Principal Component Analysis)对传感器故障的检测、确认与重构进行分析，以期获得一种可行的方案。     

A critical evaluation of variable air volume system in hot and humid climates

Although the inherent advantages of a variable air volume (VAV) system in terms of its energy performance are well understood, the designer is often challenged with complications due to different building characteristics, varying climatic conditions and multiple zoning. In hot and humid climates, it is often found that the benefits of VAV systems are not fully realized. The main objective of this paper is to compare critically the performance of a VAV system with an equivalent constant air volume (CAV) system for five different buildings in such a climate, whose internal loads remain constant throughout but the thermal loads due to the building envelope and orientation vary among the five buildings. The presence of a diversity in cooling loads is thus investigated which then leads to the exploration of potential energy savings with a VAV system. A detailed analysis of the system and plant loads and the corresponding energy consumption for the two types of systems is presented and the extent of energy saving potential of a VAV system is identified.     

基于差分主成分分析的特高压输变电工程线路施工道路水土保持监测研究

特高压输变电工程具有线路路径长、扰动区域分散等特点。其线路施工道路区是水土流失防治分区之一,监督管理相对困难。为了快速准确监测特高压输变电工程线路施工道路扰动,精细管控水土流失问题,开展智能化的水土保持监测研究十分必要。本文以锡盟-胜利1000kV特高压交流输变电工程为研究对象,利用差分主成分分析法快速准确识别出施工道路,从而监测其扰动。经现场验证,差分主成分分析法提取效果与目视遥感解译的效果基本一致。该方法可以为特高压输变电工程线路施工道路水土保持监测工作提供技术支撑。     

甘肃北山芨芨采石场岩体节理特征研究

 芨芨采石场是我国高放废物处置库甘肃北山预选区有利候选地段之一。采用综合节理测量法在芨芨采石场进行详细的节理调查，共获得13 012条节理数据。根据芨芨采石场内的断层将该区域初步划分为3个岩体结构均质区和2个断层影响区。基于圆形窗口法原理编制计算程序，分析断层两侧节理的平均迹长和迹线中点面密度的变化，确定断层对节理分布的影响范围，准确划分岩体结构均质区的大小，并得出各均质区节理的平均迹长和迹线中点面密度；采用节理玫瑰花图和节理极点图法，得出各均质区节理的优势组，对各优势组的产状进行统计分析。芨芨采石场的岩体节理以陡倾角的剪节理为主，节理倾向和倾角符合正态分布；统计分析各优势组的节理间距，得出各优势组节理间距符合负指数分布。按照ISRM提出的《岩体不连续面定量描述的建议方法》(1978)，采石场各结构均质区的节理间距都属于宽间距，表明岩体完整性较好。本次研究得到芨芨采石场岩体节理基本特征的定量化参数，为岩体力学和渗流特性的深入研究提供必要的数据。     

Application of optimization techniques on lumped HVAC models for energy conservation

Heating, ventilating, and air conditioning (HVAC) systems comprise nearly one third of annual household energy consumption in the United States. HVAC energy use can be reduced by applying controls. This study applies a novel control method on a system with arbitrary steady-state and transient load distributions. The new method uses multi-dimensional interpolation between optimized control configurations for various steady-state load distributions. Demonstration of the new method on a two-room HVAC system predicts power savings for an arbitrary steady load that is nearly equivalent to that using a Variable-Air-Volume (VAV) with chiller modulation. However, the new method provides better energy savings for arbitrary transient loads: 19% energy savings over an uncontrolled system compared to energy savings of 6% for a VAV with chiller modulation. The average transient temperature deviation from setpoint using the new method is slightly better than that using VAV with chiller modulation.     

Simulation comparison of VAV and VRF air conditioning systems in an existing building for the cooling season

Performance of two widely used air conditioning (AC) systems, variable air volume (VAV) and variable refrigerant flow (VRF), in an existing office building environment under the same indoor and outdoor conditions for an entire cooling season is simulated by using two validated respective models and compared. It was observed that the indoor temperatures could not be maintained properly at the set temperature by the VAV no-reheat boxes. However, it could be maintained by the VAV boxes with reheat with a significant energy consumption penalty. It was found that the secondary components (indoor and ventilation units) of the VRF AC system promised 38.0-83.4% energy-saving potential depending on the system configuration, indoor and outdoor conditions, when compared to the secondary components (heaters and the supply fan) of the VAV AC system. Overall, it was found that the VRF AC system promised 27.1-57.9% energy-saving potentials depending on the system configuration, indoor and outdoor conditions, when compared to the VAV AC system.     

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.