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.

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.

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.