A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data

Abstract

The fault detection and diagnosis (FDD) of air handling units (AHUs) serves as a major task in building operation management and energy savings. Data-driven classification methods have gained increasing popularities considering their flexibilities and effectiveness in practice. One essential challenge in developing accurate and reliable FDD classification models is the lack of sufficient labeled data. In practice, it can be highly time-consuming, labor-intensive and sometimes even infeasible to collected sufficient labeled data for all possible faulty operations. As a result, the fault detection models developed by limited and partially labeled data may not perform well in detecting any unknown or unseen faults in AHU operations. This study investigates the value of semi-supervised learning in detecting unseen faults during AHU operations. The main idea is to adopt a self-training strategy to gradually enhance the model capability by leveraging large amounts of unlabeled data. Data experiments have been designed to evaluate the unseen fault detection performance, the impacts of key semi-supervised learning parameters and the difficulties in detecting typical AHU faults. The insights obtained are valuable for the integration of data sciences with massive building operational data for smart building management.