Accurate fault diagnosis of heating, ventilation, and air conditioning (HVAC) systems is of significant importance for maintaining normal operation, reducing energy consumption, and minimizing maintenance costs. However, in practical applications, it is challenging to obtain sufficient fault data for HVAC systems, leading to imbalanced data, where the number of fault samples is much smaller than that of normal samples. Moreover, most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy. Therefore, to address this issue, a composite neural network fault diagnosis model is proposed, which combines SMOTETomek, multi-scale one-dimensional convolutional neural networks (M1DCNN), and support vector machine (SVM). This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset, achieving a balanced number of faulty and normal data. Then, it employs the M1DCNN model to extract feature information from the augmented dataset. Finally, it replaces the original Softmax classifier with an SVM classifier for classification, thus enhancing the fault diagnosis accuracy. Using the SMOTETomek-M1DCNN-SVM method, we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10. The results demonstrate the superiority of this approach, providing a novel and promising solution for intelligent building management, with accuracy and F₁ scores of 98.45% and 100% for the RP-1043 dataset and experimental dataset, respectively.

供暖、通风和空调 (HVAC) 系统的准确故障诊断对于维持正常运行、降低能耗和最大限度降低维护成本具有重要意义。然而，在实际应用中，获取HVAC系统足够的故障数据具有挑战性，导致数据不平衡，故障样本的数量远小于正常样本的数量。而且，大多数现有的HVAC系统故障诊断方法严重依赖平衡训练集来实现较高的故障诊断精度。因此，为了解决这个问题，提出了一种结合SMOTETomek、多尺度一维卷积神经网络（M1DCNN）和支持向量机（SVM）的复合神经网络故障诊断模型。该方法首先利用 SMOTETomek 来增加不平衡数据集中的少数类样本，实现故障数据和正常数据数量的平衡。然后，它采用 M1DCNN 模型从增强数据集中提取特征信息。最后，用SVM分类器代替原来的Softmax分类器进行分类，从而提高了故障诊断的准确性。采用SMOTETomek-M1DCNN-SVM方法，在ASHRAE RP-1043数据集和不平衡比为1:10的实验数据集上进行故障诊断验证。结果证明了该方法的优越性，为智能建筑管理提供了一种新颖且有前途的解决方案，RP-1043 数据集和实验数据集的 准确率和 F _{1分数分别为 98.45% 和 100%。}