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。     

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。     

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。     

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。     

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。     

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。     

基于经验模态分解去噪改进主成分分析的冷水机组传感器故障检测

提出了一种基于经验模态分解(EMD)阈值去噪(TD)和主成分分析(PCA)相结合的冷水机组传感器故障检测方法(EMD-TD-PCA)。采用EMD阈值去噪法去除原始数据中的噪声来提高数据质量,针对去噪后的数据构建PCA模型。采集了武汉市某电子厂螺杆式冷水机组的实际运行数据,用于验证故障检测效果,并与传统PCA方法和小波阈值去噪(Wavelet-TD-PCA)方法的传感器故障检测结果进行了对比。结果表明:EMD-TD-PCA可以有效提高冷水机组传感器的故障检测效率,同等偏差条件下,故障检测效果优于传统PCA方法和Wavelet-TD-PCA方法。对于小偏差(-1～1℃)故障,故障检测效果提升尤为明显。     

利用少量传感器信息与人工智能的桥梁结构安全监测新方法

分析了当前结构安全监测在工程应用中存在的诸多问题和挑战,提出应发展利用少量传感器信息及基于大数据与人工智能的安全监测新方法,来克服现有系统传感器繁多、造价昂贵、海量数据难以处理的问题。介绍了单测点信息的多维相空间方法和单传感器信息的重构相空间方法;在基于双传感器信息的移动互相关函数法基础上,提出了基于双传感器信息的移动传递熵方法;阐述了基于少量传感器信息的移动主成分分析法的物理意义及其工程应用的适用性和可行性。利用单传感器方法和移动主成分分析法,以及小波包能量法、二次协方差矩阵法对虎门大桥进行长达5年的安全监测。阐述了基于深度学习的结构安全监测人工智能方法及其发展概况,分析了基于结构动力学信息的深度学习方法及其巨大的应用潜力。在此基础上进一步思考了基于少量传感器和人工智能相结合的方法在结构安全监测应用中的发展思路。结果表明:单传感器信息的重构相空间方法更适用于实际工程;基于双传感器信息的移动互相关函数法和移动传递熵方法均能精确定位损伤;移动主成分分析法最适用于实际工程的实时监测。     

基于WE-ICA的牵引电机速度传感器微小故障检测与识别

文章提出了一种基于小波变换和集成独立成分分析(WE-ICA)的牵引电机速度传感器微小故障检测与识别方法。首先,通过小波变换对高速列车牵引电机数据进行滤波处理,减少噪声对微小故障信息的干扰;接着通过独立成分分析(ICA)方法提取故障信息并建立检测统计量;最后利用贝叶斯推理计算统计量的故障概率,并设计集成统计量用于微小故障检测;另外通过加权贡献度对故障进行识别。利用CRH2牵引电机实验平台对该方法进行验证,实验结果证明了该方法的有效性和实用性。     

小波滤波在小故障检测中的应用

为了提高小故障检测能力,对基于主成分分析的方法进行了改进,用小波滤波法对数据进行过滤。实验表明,小波滤波法能提高小故障的检测能力,而且与指数权重滑动平均滤波法相比更具有故障检测的及时性。     

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 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 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.     

基于模糊神经网络的热泵空调故障诊断研究

将模糊理论与神经网络相结合,构成了模糊神经网络;针对热泵空调五种常见故障,分析了模糊神经网络在热泵空调故障诊断中的应用。与传统故障诊断方法相比,基于模糊神经网络的故障诊断方法对单一故障具有很好的识别能力,可以提高诊断精度。     

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.     

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。