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Data-driven approach has emerged as a popular research method for fault detection and diagnosis (FDD) of variable refrigerant flow system. However, one serious issue has been raised in recent studies, which shows that training classifiers with datasets which suffer of imbalanced class distributions can significantly influence the final classification accuracy. So, aiming at the imbalanced dataset environment problem of variable refrigerant flow (VRF) system based on data-driven fault detect and diagnosis, we conducted a study. First of all, through comparative research, it is concluded that the geometric mean accuracy of the fault detection in the VRF system has reduced an average of 42.49% due to the imbalanced data environment. Then we proposed a Principal Component Analysis-Synthetic Minority Oversampling Technique (PCA-SMOTE) method to save this problem, and verified the reliability and versatility of the proposed method with three types of modeling algorithms commonly used in VRF systems. The result shows that this method not only has obvious improvement effect, but also has strong versatility. In the end, we gradually reduce the number of minority from 50 to 10 to explore minimum fault data requirements of this method. The final result shows that this method can still ensure that common machine learning algorithms can effectively detect and diagnosis the VRF system fault when only 10 fault data are included. [ABSTRACT FROM AUTHOR]

• A novel fault diagnosis strategy is proposed using the GMM method. • The principal component analysis is selected for data dimensionality reduction. • Evaluations are made using four faults data of the VRF system experiments. • The established PCA-GMM has better fault diagnosis performance and reduced runtime. The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7. [ABSTRACT FROM AUTHOR]

• 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]