Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

Fault isolation of air handling unit based on neural network

Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.

Novel Approach for Fault Detection and Diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

A novel fault diagnosis method for sensors in air handling unit（AHU） using wavelet energy entropy was presented. Instead of directly comparing the numerous data under noise conditiom, the wavelet energy entropy residual was compared in the proposed method. Three.level wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions. The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients. The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold. Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults. The wavelet energy entropy residuals exceed the threshold significantly when faults occur. In addition, the severer the faults were, the larger the residuals would be. The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.

Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis

Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major challenges.Firstly,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further validation.Secondly,the interpretability performance of DL models under various information scenarios needs further exploration.To address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information scenarios.Furthermore,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN models.An R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the models.The results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information scenarios.For the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s interpretation.Finally,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative results.This study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpretability of LRP-based CNN models for AHU FD,which helps provide improved reliability of DL models in practical applications.

Feasibility of condensate water recycle of split air handling unit in Chinese University buildings

Recycling the condensate water of the air conditioner could be explored as an alternative water source to con-tribute to building the green campus.This paper explored the condensate water production through actual mea-surement based on a split air handling unit(SAHU)in a university building.Then,the statistical analysis was used to analyze the recycling feasibility and the impact factors of the condensate water production in 31 Chinese provincial capital cities to obtain the recycling potential map of the condensate water generated from a SAHU.Results showed that:(1)In the measurement,the amount of condensate water produced by a single split air conditioner was 1.6 kg from 12:40 to 13:40.Therefore,the daily output of condensate water of the air condi-tioner with the university operation schedule could reach 52.99 kg during the main air-conditioning season.(2)Among the 31 provincial capital cities in China,the largest condensate water outputs could be found in the Hot Summer and Warm Winter zone and the Hot Summer and Cold Winter zone,with an average monthly output of 1600 kg and 1100 kg,respectively.(3)Regression analysis showed that the dry-bulb temperature and dew point temperature of outdoor air had the highest positive and significant influence on condensate water production.The objective of this study is to provide theoretical guidance for the design and energy conservation evaluation of the feasibility of SAHU condensate water recycling in universities.

基于AHU的液冷智算中心标准化建设方案研究

随着人工智能技术的突飞猛进,智算中心(AIDC)的建设迎来了前所未有的发展机遇。本文通过对智算中心的发展现状和技术要求进行深入分析,并结合“东数西算”西部算力节点的气候特点,研究并制定出了一套基于间接蒸发冷却(AHU)和液冷制冷技术的智算中心标准化建设方案。该方案全面覆盖土建、电源、制冷和工艺布局等关键领域,并通过实际项目应用验证了其可行性和实用性。

Tracking controller design for air handling units with uncertainties

As an important component of the heating,ventilating and air conditioning(HVAC)systems,air handling units(AHUs)are responsible for regulating indoor temperature and humidity.In this paper,a multivariable nonlinear dynamic model of the AHUs with unknown strength of the humidity source is considered,and an improved backstepping controller is proposed to realize the tracking objective of the indoor temperature,relative humidity and carbon dioxide concentration.Firstly,the original system is represented in simplified state space form,and then the state transformation is introduced with a gain to overcome the difficulty caused by the unknown strength of the humidity source.Then,the improved backstepping controller is designed in a step-by-step way.Moreover,the stability of the closed-loop system is analyzed in detail.Finally,we consider the case that the AHUs work in summer of Jinan,China,as an example.The simulation results show the effectiveness of the controller.Meanwhile,the performance of the improved backstepping controller are compared with that of the decoupled sliding mode and PID controllers.

贫数据中基于模型自训练的空气处理设备故障诊断

针对空气处理设备(AHU)故障贫数据,基于深度置信网络(DBN)模型对4种特征选择算法进行对比研究,结果表明最大相关最小冗余算法的特征子集在诊断准确率及子集元素稳定性上表现最优。提出将DBN嵌入自训练框架的故障诊断模型,发现DBN自训练的诊断准确率较单纯DBN最高可提升19.5%。提出均匀抽样及按比例抽样2种自训练伪标签抽样策略,二者的诊断准确率均随抽样数减小而增大,在不同抽样数中的最大差异为3.42%;在所有贫数据样本中,均匀抽样策略始终优于按比例抽样,诊断准确率最大相差1.39%,表明在故障标签匮乏时,采用均匀抽样策略及较小的抽样数有利于提升DBN自训练的诊断性能。

数据机房室外AHU机组及柴发机组的气流组织分析

目前,数据中心建设正处于高速发展时期,各地政府部门也给予了大力扶持,这为数据中心行业的发展带来了巨大优势。随着数据中心行业的飞速发展,一些大中型的数据中心也会越来越多。由于数据机房散热量巨大,故需要机房空调将大量热量从数据中心转移出去,此时室外机组的散热尤为重要。为避免室外AHU机组附近热量堆积,其周围需要合理的气流组织尽快将热量带走。以某数据机房为研究对象,借助CFD软件对室外AHU机组及柴发机组的气流进行仿真模拟,判断不同典型工况下AHU机组及柴发机组的设置是否满足生产要求,并给出合理化建议。

基于反步法的空气处理机组温湿度跟踪控制

针对空气处理机组室内湿负荷和进回水温差不确定的情况,提出一种带有多参数的反步控制器,可以实现温度和湿度的跟踪控制。为了设计方便,先将空气处理机组的动态模型转换为状态空间表达形式,通过引入带增益的状态变换,将原系统的跟踪控制问题转换为变换后所得新系统的稳定控制问题;利用反步法逐步设计了控制器,并通过选择合适的设计参数,实现了温度和湿度的跟踪控制目标;分析了闭环系统的稳定性并给出了证明过程。结果表明:仿真实验验证了所研控制器的有效性,通过与PID控制器对比可知,所设计控制器的跟踪效果更好。

AHU送风温度变化特性的分形关联维数分析

针对定量模型方法对系统性能参数进行评价时,提出采用分形关联维数代替传统的直接比较方法。以空气处理机组(Air handling unit,AHU)送风温度为例,在三种负荷和四种噪声下对温度变化特性进行分形关联维数分析。瞬态系统(Transient system,TRNSYS)仿真平台提供送风温度测量值,AHU过程模型提供模拟值。结果表明,分形关联维数只用一个维数值就可以表征参数曲线的变化特性,能够大大减少比较参数对的数目。同时,噪声区间超过一定值后对分形关联维数计算值的影响很大,必须进行降噪处理,使相同条件下的测量关联维数计算值接近或等于模拟关联维数值。并给出不必对AHU送风温度曲线进行降噪处理的最小噪声区间。

Impact of Varying Blower Opening Degrees on Indoor Environment and Thermal Comfort

At present,air handling units are usually used indoors to improve the indoor environment quality.However,while introducing fresh air to improve air quality,air velocity has a certain impact on the occupants’thermal comfort.Therefore,it is necessary to explore the optimization of air-fluid-body interaction dynamics.In this study,the indoor air flow was changed by changing the opening and closing degree of the blower,and the thermal manikin is introduced to objectively evaluate the human thermal comfort under different air velocities.The main experimental results show that the air change rate increases with the increase of the opening and closing degree of the blower considering an ACH(air changes per hour)range between 3.8 and 10.For a better prediction,a linear correlation with a coefficient of 0.995 is proposed.As the blower’s opening is adjusted to 20%,25%,30%,35%,and 40%,the air velocity sensor positioned directly beneath the air inlet records average velocities of 0.19,0.20,0.21,0.28,and 0.34 m/s over four hours,respectively.Observations on thermal comfort and the average sensation experienced by individuals indicate an initial increase followed by a decline when the blower’s operation begins,with optimal conditions achieved at a 35%opening.These findings offer valuable insights for future indoor air ventilation and heat transfer design strategies.

变风量空调系统的优化控制

从变风量末端装置控制、空调机组送风温度控制、空调机组风机转速控制、空调系统新风量控制4个方面介绍了变风量空调系统的优化控制原理。每个方面都从控制目标、控制对象、控制逻辑明确了各控制环节要做什么、如何做好的关键环节。

冷水机组在空分装置运行中的重要性

冷水机组是大型空分装置不可或缺的关键设备,具有降低空冷塔出口空气温度、保证纯化器吸附效果的作用。当冷水机组突发故障,会导致空气温度升高,纯化器吸附负荷增大,处理时间过长,造成纯化器分子筛床层击穿、不合格空气进入冷箱、板式换热器冻堵被迫停车回温等严重后果。通过一次冷水机组事故分析及理论计算,计算出事故工况下纯化器水分及二氧化碳吸附饱和的时间,最终得出冷水机组对空分装置长周期运行的重要性及增加备机的必要性。

2023年我国制冷空调行业市场分析

通过对我国制冷空调市场统计数据的全面分析,详细阐述2023年我国制冷空调市场发展情况及变化趋势。2023年,在高质量满足市场需求的同时,我国制冷空调行业深入贯彻落实新发展理念,不仅主要指标再创历史新高,而且实现了质的有效提升。2024年,我国经济将稳步扎实发展,对制冷空调产品将呈现多样化的需求,预计制冷空调行业总体将保持与国民经济同步发展,并逐渐加大提质增效步伐。

舒适性空调系统AHU送风温度优化仿真研究

为解决空调机组(AHU)变风量运行方式下送风温度难以根据工况变化进行动态优化的问题,提出了一种基于PSO算法的空调机组送风温度的优化方法,该方法对空调机组运行能耗进行分解,分别建立风机能耗模型和水泵能耗模型,并以空调机组运行能耗最小为目标,以表冷器的运行性能为约束,对不同工况下空调机组的最优送风温度进行求解。仿真结果表明,该方法能有效的应用于AHU变风量运行参数的优化,与常规变风量运行方式相比具有17.72%的节能效果。

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、冷冻水流量三种故障中的检测率均大于90%。使用BP神经网络算法对系统故障进行诊断,新风温度传感器故障和冷冻水流量传感器故障诊断率均达到了100%。新风温度传感器由于精度较高检测率为92%。但是三种故障类型检测率都超过了90%。通过分析表明改进型主元分析(PCA)和BP神经网络算法可以有效检测与诊断AHU系统故障。

核级AHU空气热湿处理过程现场测定方法

针对核电站用核级AHU空气热湿处理过程中难以按设计工况进行调试的问题,提出适用于核岛通风空调系统核级AHU空气热湿处理过程现场测定方法,并验证该测定方法的可行性和适用性。结果表明,该方法可推广应用于其他核电站同类设备的测试评定,为核电现场调试人员对AHU的实际冷却能力或加热能力进行现场调试测定评定提供参考,判定AHU不在设计工况点条件下的热湿处理能力是否符合设计输入指标要求。

空气处理机组AHU在电子工厂应用的案例分析

某工厂的充放电工段中AHU的设计容量小于实际生产时的容量,导致产线充放电区域最高温度高达43±3℃。通过对AHU内部新安装冷水盘管、在充放电设备排热端安装PC实心板、在充放电设备两侧安装聚氨酯库板等3个技术改造,将环境温度降至温度管理基准29±3℃内,降低了充放电设备的元器件损坏率,实现了充放电设备的稳定运行,生产架动率由20%提高到了95%。

高分子膜式新风机组换热特性研究

研究以聚丙烯腈、硅氧烷酰胺和氯化锂为原料制备了复合膜,并以此为热交换膜制作了全热交换装置。通过试验研究了复合膜水蒸气透过率的变化规律,结果表明:复合膜对水蒸气的透过率最高可达39.5×10^(-8)kg/(m^(2)sPa)。当风量为680m^(3)/h时,送风净新风率为98.1%,达到C2泄漏等级。全热回收效率随着风量的增加而减小,在冬季工况条件下,风量为213m^(3)/h时,该装置的全热交换效率可达81.5%。