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.

暖通空调系统的及时故障诊断对于建筑物节能，设备维护和室内舒适度至关重要。高斯混合模型方法在HVAC系统的故障诊断应用中很少研究。因此，提出了一种基于高斯混合模型（GMM）方法的变制冷剂流量空调系统故障诊断策略。为了减少过多的输入变量，从而导致较大的模型复杂性和较长的运行时间，主成分分析方法（PCA）用于执行数据维数减少。因此，建立了将高斯混合模型和主成分分析相结合的故障诊断模型，并使用可变制冷剂流量系统的四种类型的故障进行评估。这些故障包括制冷剂充注不足，制冷剂过充，室外机结垢和四通换向阀故障。在三种加热条件下进行实验。结果表明，PCA-GMM方法可以有效减少运行时间。特别是对于VVV型型号，运行时间从176.78 s减少到15.18 s。同时，当减小输入数据维时，已建立的PCA-GMM仍然具有良好的故障诊断正确率。特别是，当主成分数超过7时，某些PCA-GMM的故障诊断正确率超过99％。同时，当减小输入数据维时，已建立的PCA-GMM仍然具有良好的故障诊断正确率。特别是，当主成分数超过7时，某些PCA-GMM的故障诊断正确率超过99％。同时，当减小输入数据维时，已建立的PCA-GMM仍然具有良好的故障诊断正确率。特别是，当主成分数超过7时，某些PCA-GMM的故障诊断正确率超过99％。