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An intelligent fault diagnosis network for variable refrigerant flow air conditioning system is proposed in this study. The network is developed under the foundation of bayesian belief network theory, which comprises two main elements: the structure and parameters. The structure obtained by machine learning and experts’ experiences illustrates the relationships among faults and physical variables from the qualitative prospective, and its parameters (including prior probability distribution and conditional distribution) describe the uncertainty between them quantitatively. Once the structure and parameters are determined, the posterior probability distribution which can be used to complete fault diagnosis and isolation will be calculated by some algorithms. In comparison with other fault diagnosis approaches, the proposed approach can make full use of performance information. Moreover, it is more reasonable and precise to express the relationship between faults and variables rather than Boolean variables. Evaluation was conducted on a variable refrigerant flow air conditioning system, which demonstrated that this strategy is effective and efficient. [ABSTRACT FROM AUTHOR]

• 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. • 1-D CNN models achieves good accuracy and is capable of fault diagnosis. • Enhanced the method to an ensemble model by using parallel 1-D CNNs. • The ensemble model's performance is improved and archived state-of-the-art accuracy. Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches. [ABSTRACT FROM AUTHOR]

• The influences of various common faults on the VRFs under different fault levels and load rates was analyzed. • A faults experienced database for VRFs was provided. • The fault mechanism was clarified through sensitivity analysis between the physical parameters and different faults. • A reference basis for selecting various fault features and key fault decoupling variables was provided when a multiple fault diagnosis model for VRFs is established. Due to improper installation, operation, and maintenance, many common faults inevitably occur in the air conditioning systems. However, the same fault could demonstrate different characteristics under different load rates and fault levels; different faults may present similar fault phenomena. This paper reveals the quantitative impact of faults on system performance of VRF system with common faults imposed in heating mode through fault experiments. The system parameters sensitivity study has been carried out to understand the internal impact mechanism related to system parameters. This provides a reference for selecting characteristic parameters and fault decoupling variables and developing interpretable, professional, and reliable fault detection and diagnosis models. The results show that the outdoor unit fouling fault can cause the heating capacity to drop by 73.2%, and the indoor unit fouling can cause the system power to drop by 65.5%. The coefficient of performance drop is as high as 48.6%. The sensitivity study result shows that the system load rate is an influential parameter; it needs to be considered when characteristic parameters and fault decoupling variables are selected. [ABSTRACT FROM AUTHOR]

• A combined generative network is built based on VAE and WGAN-GP. • The ensemble classifiers are embedded into generative network for FDD. • A detailed comparison between SMOTE and generative network method is discussed. Faults in building Heating, Ventilation, and Air-condition (HVAC) system create an uncomfortable indoor environment and cause energy waste. The data-driven method has been widely applied for Fault Detection and Diagnosis (FDD) in the complex building HVAC system. This method relies on the availability of many fault data which is difficult to collect. This makes it quite challenging to apply the data-driven methods for the FDD of the HVAC system. Thus, a novel data-driven FDD method that only utilizes small fault data collected from a Variable Refrigerant Flow air condition system has been proposed. Under different conditions, the fault and normal data are collected in an enthalpy difference laboratory to create small and imbalanced data. A generative network is developed by combining Wasserstein Generative Adversarial Network with Gradient Penalty and Variational Auto-Encoder. To improve the FDD classifier's accuracy and to train an end-to-end network model using small and imbalanced data, two ensemble classifiers are embedded into the generative network. The dataset includes normal and fault data have been applied to train the modified generative network, and two ensemble classifiers are used to detect and diagnose the fault, respectively. The performance indexes show that the proposed method is much better than the SMOTE-based methods in almost all training groups. Besides, the comparison between the proposed method and generative network with a single classifier indicates that the ensemble classifiers can improve the F1-score of fault detection and the accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel thermoelectric radiant air-conditioning (TE-RAC) system, which employs the thermoelectric modules as radiant panels instead of conventional hydronic panels. The novel radiant system brings a lot of benefits including Freon free, convenient installation, no complex water distribution pipes, quiet and reliable operation, etc. Switching between cooling and heating modes can be easily achieved by reversing the input current. The purpose of this study is to investigate the feasibility and performance of the novel TE-RAC system, including its cooling and heating performances, condensation risk, dynamic thermal response and inertia, and cost-effectiveness. A case study of the TE-RAC system is presented to compare the TE-RAC system with conventional air-conditioning (AC) systems and radiant air-conditioning (RAC) systems in terms of cooling and heating capacities, energy consumption and operation reliability. It is found that the COP of the TE-RAC system could be comparable to conventional RAC systems and AC systems, besides many other benefits in life-cycle performances. [Copyright &y& Elsevier]

• The proposed strategy aims at system- and component-level fault types of VRF systems. • An infrequent fault type of VRF systems is studied. • The FDD strategy is based on hybrid data mining methods. • The results are explained and supported by thermodynamics theory. • The established models can accurately detect and diagnose faults of VRF systems. VRF systems are always vulnerable to kinds of faults. Fault detection and diagnosis research should not only accurately identify these faults, but also be capable of obtaining explanations and support in thermodynamic theory. In this study, a strategy is proposed for four types of VRF system faults, including system-level and component-level. The strategy is based on hybrid data mining methods and analyzes the thermodynamic interpretation of the results at the same time. The first preprocessing step eliminates the effect of noise caused by defrosting action in heating mode. We apply unsupervised principal component analysis for feature extraction to reduce the dimensions of data sets. The correlation between principal components and features are investigated. Supervised Gauss naïve Bayes is used to establish the fault detection model with an accuracy of 98.6%. Besides, infrequent fault type is often difficult to be studied because of lacking sufficient data. Therefore, RUSBoost algorithm is used to solve the unbalanced set problem, and the results show enough competitiveness in the comparison of similar algorithms and online testing. Conclusive remarks confirm the truth that the proposed strategy enjoys high versatility, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

To address the problem that the energy consumption of chiller is affected by multiple factors and lacking of abnormal detection model which can be used for energy consumption of chillers, an integrated model based on Partitioning around medoids(PAM), Support Vector Classification (SVC) and 3-σ rules is proposed in this study. In the detection of abnormal energy consumption of the chillers, the observed data of a Heating Ventilation and Air Conditioning (HVAC) system from Hubei province, China are used to develop and evaluate the integrated model. PAM is applied to divide consumption patterns of the historical data. SVC is applied to recognize consumption patterns of the testing data. 3-σ rules are used for abnormal detection. The results show that PAM divide the energy consumption into four patterns, SVC multi-classifier can effectively recognize energy consumption patterns with an accuracy reaches 98.29%. Compared with the popular model, the integrated learning model applied for the detection of abnormal energy consumption reaches a twice as high as the 40% detection success rate of the 3-σ rules. Moreover, the patterns recognition component can be used to understand the people's energy consumption behavior and the anomaly detection component can be used to regulate the energy usage. [ABSTRACT FROM AUTHOR]

• We propose a comparison study of basic data-driven fault diagnosis for VRF system. • The performance under single fault and multiple faults is tested and compared. • DNN is preferred for multiple faults, and SVM is selected for single faults. • All method has lower HR for F5 and F6 than the rest in the multiple fault diagnosis. • We came up with suggestions based on comparative analysis for subsequent research. HVAC systems occupy a large part of the building's energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper. [ABSTRACT FROM AUTHOR]