Variable refrigerant flow (VRF) systems have gained much attention and been widely used in commercial and residential buildings benefitting from their competitive advantages. However, after long-term operation in a complex environment, various faults may occur in the VRF systems, resulting in failure to meet users comfort requirements and even unnecessary increase in energy consumption. This paper proposes a simulated annealing wrapped generic ensemble fault diagnosis strategy for typical faults of VRF systems, such as refrigerant charge amount (RCA) faults, valve faults, and compressor liquid return (LF) faults. The simulated annealing algorithm based on random forest (SA-RF) is first utilized to perform feature selection process on the three kinds of fault datasets to select the optimal variables that can well characterize the fault states, which can improve the modeling efficiency while reducing the data dimensionality. Then five component learners and the proposed ensemble model based on them are established adopting the optimal variables as input variables. Through visualizing the error evolution and margin of the boosting models built in the first stage of the integration process, it was found that the boosting models can effectively avoid overfitting and most samples are correctly classified with high confidence. By comparing with the five component learners, it is concluded that the boosting strategy in the first stage can improve the diagnostic performance of the models, and the weighted voting integration strategy in the second stage can further improve the diagnostic performance of the model. The final ensemble model can effectively compensate for the deficiencies of each component learners and its diagnostic accuracy for the three fault data sets is as high as 95.37%, 99.36% and 98.3%, respectively, indicating that the model can be applied to diagnose the three types of faults in VRF system at the same time, showing a high versatility.

可变制冷剂流量（VRF）系统已经赢得了广泛的关注，并受益于它们的竞争优势而广泛用于商业和住宅建筑。但是，在复杂环境中长期运行后，VRF系统可能会发生各种故障，导致无法满足用户的舒适度要求，甚至导致不必要的能耗增加。本文针对VRF系统的典型故障，例如制冷剂充注量（RCA）故障，阀故障和压缩机回液（LF）故障，提出了一种模拟退火包装的通用整体故障诊断策略。首先使用基于随机森林的模拟退火算法（SA-RF）对三种故障数据集进行特征选择过程，以选择能够很好地表征故障状态的最佳变量，这可以提高建模效率，同时降低数据维数。然后，以最优变量为输入变量，建立了五种成分学习器并提出了基于它们的集成模型。通过可视化在集成过程的第一阶段中建立的增强模型的误差演化和裕度，发现增强模型可以有效地避免过度拟合，并且大多数样本都可以以高置信度正确分类。通过与五个学习者的比较，可以得出结论，第一阶段的提升策略可以提高模型的诊断性能，第二阶段的加权投票整合策略可以进一步提高模型的诊断性能。