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。     

基于小波变换的传感器故障诊断研究

主元分析法可以应用于空调系统的传感器故障诊断,但是测量数据中隐含的噪声及系统的动态性影响了这一方法在故障诊断时的效果。本文提出采用小波变换的方法对测量数据进行分解,利用不含噪声及动态性的低频信号进行传感器的故障诊断,即基于小波变换的主元分析故障诊断法。该方法采用一大型离心式制冷机的实测运行数据进行验证,且同时与常规的主元分析法进行比较,结果说明基于小波变换的主元分析法可以提高故障诊断水平。     

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。     

汽车电控悬架系统的传感器故障及其检修技术研究

电控悬架系统可以有效控制悬架的状态,满足汽车的行驶性能能。车辆振动对电控悬架系统部件的性能影响较大,传感器一旦发生故障将会失去对车辆行驶状态的准确判断,使得电控悬架系统无法正常工作。因此,面对传感器所存在的故障,针对汽车电控悬架系统的传感器故障为切入点,通过建模分析的方式对传感器故障诊断的方法进行研究。     

基于主元分析的冷水机组传感器故障识别

测量数据的真实性和准确性是冷水机组安全运行和优化节能的必要条件。长期使用条件下,传感器故障极易发生而且很难识别。在基于主元分析的传感器故障研究中,以Q统计量为检测指标的常规故障检测、诊断与数据重构在故障源的识别上存在一定误判。采用基于数据重构的枚举甄别方法,分析了传感器故障检测、数据重构及故障识别的算法流程,并以实际工程数据进行验证。结果表明,问题传感器的重构数据的故障识别指标变化最明显,可以准确鉴别故障传感器。     

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。     

基于累积残差贡献率的传感器故障定位方法

为准确定位结构健康监测系统中的故障传感器,提出了基于累积残差贡献率的传感器故障定位方法。基于主元分析的基本原理,将车辆荷载和地脉动激励下传感器采集的数据分为主元空间和残差空间,采用SPE统计量对故障进行识别。在此基础上,通过对残差贡献值的推导,提出了累积残差贡献率指标,改进了现有的残差贡献图,提高了故障定位的准确率,并将单传感器故障定位拓展到两个故障传感器的同时定位。数值模拟结果表明:主元分析法能准确识别出预设的4类常见传感器故障,累积残差贡献率不但能更好地定位单传感器故障,两传感器同时发生故障时也能准确定位。     

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。     

基于数据驱动的变风量空调AHU故障诊断研究

变风量空调系统的空气处理机组(AHU)出现故障时会使系统舒适性降低,能耗和运维成本增加.本文提出了一种基于改进型主元分析(PCA)和BP神经网络算法,用于AHU的模型建立及故障诊断.结果表明使用改进滤波的PCA检测模型主元数为3个,累计贡献率92.7％.当系统传感器出现5％的故障偏差,模型在送风温度、 新风温度、 冷冻...     

主元数量对故障传感器重构精度影响分析

传感器正常工作是冷水机组安全运行和优化节能的必要条件,但随着使用年限的增加,各类传感器故障时常发生。以Q统计量为检测指标的主元分析方法,常用于传感器故障检测、诊断与数据重构。由于主元数量的选取对于主元空间和残差空间的投影过程的建立具有较大影响,分析了不同主元数量对重构精度的影响规律。结果表明,主元数量越多,传感器重构数据的精度越高。     

冷水机组冷凝器水侧受阻故障模拟实验研究

采用理论分析和实验测试方法,对冷水机组冷凝器水侧受阻故障时空调系统冷水侧、制冷剂侧、冷却水侧的运行参数进行研究,确定对冷水机组冷凝器水侧受阻故障敏感的运行参数。冷却水出水温度、制冷剂冷凝温度、压缩机排气温度是对冷水机组冷凝器水侧受阻故障敏感的运行参数,可以作为检测识别该故障的主要依据。     

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.     

A fault detection technique for air-source heat pump water chiller/heaters

This paper describes a fault detection method and system to detect the faults in air-source heat pump water chiller/heaters. Principal component analysis (PCA) approach is used to extract the correlation of variables in heat pump unit and reduce the dimension of measured data. A PCA model is built to determine the thresholds of statistics and calculate square prediction errors (SPE) of new observations, which are used to check if a fault occurs in heat pump unit. The fault detection system consists of a PCA-based fault detection code, a backpack computer, a digital logger and eight easy-to-install temperature sensors. A real air-source heat pump water chiller/heater for the air-conditioning system of an office building provides the realistic test platform for the validation of fault detection method. The measured data from the heat pump unit under normal condition shows that the PCA model can capture the major correlation and variance among the test variables. Two levels of artificial condenser fouling fault are successfully detected. The results show that the PCA-based fault detection method is applicable and effective for air-source heat pump water chiller/heater.     

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.     

大型制冷机组故障调查与分析

介绍了制冷机组故障调查的方法、手段、时间、范围等,使用多种方法调查了制冷机组各部件的故障发生情况.同时,应用可靠性理论和故障率理论分析和总结了制冷机组的故障分布规律.所进行的调查和分析可为我国大型制冷机组行业提供有益的经验和基础数据.     

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 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.