In practical chiller systems, applying efficient fault diagnosis techniques can significantly reduce energy consumption and improve energy efficiency of buildings. The success of the existing methods for fault diagnosis of chillers relies on the condition that sufficient labeled data are available for training. However, label acquisition is laborious and costly in practice. Usually, the number of labeled data is limited and most data available are unlabeled. Most of the existing methods cannot exploit the information contained in unlabeled data, which significantly limits the improvement of fault diagnosis performance in chiller systems. To make effective use of unlabeled data to further improve fault diagnosis performance and reduce the dependency on labeled data, we proposed a novel semi-supervised data-driven fault diagnosis method for chiller systems based on the semi-generative adversarial network, which incorporates both unlabeled and labeled data into learning process. The semi-generative adversarial network can learn the information of data distribution from unlabeled data and this information can help to significantly improve the diagnostic performance. Experimental results demonstrate the effectiveness of the proposed method. Under the scenario that there are only 80 labeled samples and 16,000 unlabeled samples, the proposed method can improve the diagnostic accuracy to 84%, while the supervised baseline methods only reach the accuracy of 65% at most. Besides, compared with the supervised learning method based on the neural network, the proposed semi-supervised method can reduce the minimal required number of labeled samples by about 60% when there are enough unlabeled samples.

在实际的冷却系统中，应用有效的故障诊断技术可以显着降低能耗并提高建筑物的能效。现有的冷水机故障诊断方法的成功取决于有足够的标记数据可供训练的条件。但是，在实践中，获取标签很费力且昂贵。通常，标记数据的数量是有限的，并且大多数可用数据都是未标记的。大多数现有方法无法利用未标记数据中包含的信息，这极大地限制了冷却器系统中故障诊断性能的提高。为了有效利用未标记的数据来进一步提高故障诊断性能并减少对标记数据的依赖性，我们提出了一种基于半生成对抗网络的冷水机系统半监督数据驱动故障诊断新方法，该方法将未标记和标记的数据都整合到学习过程中。半生成式对抗网络可以从未标记的数据中了解数据分发的信息，并且该信息可以帮助显着提高诊断性能。实验结果证明了该方法的有效性。在只有80个标记样品和16,000个未标记样品的情况下，所提出的方法可以将诊断准确性提高到84％，而监督基线方法最多只能达到65％的准确性。此外，与基于神经网络的监督学习方法相比，