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.

可变制冷剂流量（VRF）系统是被广泛采用的空调系统。当VRF系统中发生系统故障时，VRF系统的效率将急剧下降。本文提出了一个一维CNN模型和一个带有并行一维CNN的集成模型，用于诊断加热条件下的VRF系统制冷剂充注故障。从商业VRF系统的清洗实验数据中，选择15个特征作为使用ReliefF算法的建议模型的输入。训练后，评估单个一维CNN模型和整体一维CNN模型的诊断准确性，并将其与BPNN模型和DT模型的诊断准确性进行比较。结果表明，单个一维CNN模型和整体一维CNN模型都可以有效地诊断VRF系统制冷剂充注故障。在建议的模型中也可以实现故障检测。集成一维CNN模型的9级制冷剂充注故障的平均诊断准确率高达97.4％，超过了BPNN模型，SVM模型，DT模式和DBN模型。基于一维CNN的模型首次用于VRF系统故障诊断，为相关研究的扩展奠定了基础。