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.

VRF 系统总是容易受到各种故障的影响。故障检测与诊断研究不仅要准确识别这些故障，还要能够获得热力学理论的解释和支持。在这项研究中，针对四种类型的 VRF 系统故障提出了一种策略，包括系统级和组件级。该策略基于混合数据挖掘方法，同时分析结果的热力学解释。第一个预处理步骤消除了加热模式下除霜动作引起的噪音影响。我们应用无监督主成分分析进行特征提取以减少数据集的维度。研究了主成分和特征之间的相关性。使用监督高斯朴素贝叶斯建立故障检测模型，准确率为98.6%。此外，由于缺乏足够的数据，罕见的故障类型往往难以研究。因此，采用RUSBoost算法解决不平衡集问题，结果在同类算法对比和在线测试中显示出足够的竞争力。结论性评论证实了所提出的策略具有高度通用性、准确性和稳健性的事实。