A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data

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.

Automated summary

A novel semi-supervised data-driven fault diagnosis method for chiller systems based on semi-generative adversarial networks is proposed to improve diagnostic performance using unlabeled data and reduce dependency on labeled data. Experimental results show that the proposed method significantly enhances diagnostic accuracy, even with limited labeled samples and vast unlabeled samples.