Semi-Supervised Random Forest Methodology for Fault Diagnosis in Air-Handling Units

Air-handling units have been widely used in indoor air conditioning and circulation in modern buildings. The data-driven FDD method has been widely used in the field of industrial roads, and has been widely welcomed because of its extensiveness and flexibility in practical applications. Under the condition of sufficient labeled data, previous studies have verified the utility and value of various supervised learning algorithms in FDD tasks. However, in practice, obtaining sufficient labeled data can be very challenging, expensive, and will consume a lot of time and manpower, making it difficult or even impractical to fully explore the potential of supervised learning algorithms. To solve this problem, this study proposes a semi-supervised FDD method based on random forest. This method adopts a self-training strategy for semi-supervised learning and has been verified in two practical applications: fault diagnosis and fault detection. Through a large number of data experiments, the influence of key learning parameters is statistically represented, including the availability of marked data, the number of iterations of maximum half-supervised learning, and the threshold of utilization of pseudo-label data. The results show that the proposed method can effectively utilize a large number of unlabeled data, improve the generalization performance of the model, and improve the diagnostic accuracy of different column categories by about 10%. The results are helpful for the development of advanced data-driven fault detection and diagnosis tools for intelligent building systems.

Automated summary

This study proposes a semi-supervised FDD method based on random forest, which has been verified in two practical applications. The results show that the proposed method can effectively utilize a large amount of unlabeled data, improve the generalization performance of the model, and improve the diagnostic accuracy of different column categories by about 10%. These results are helpful for the development of advanced data-driven fault detection and diagnosis tools for intelligent building systems.