Bayesian network-based fault detection and diagnosis of heating components in heat recovery ventilation

¹ Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, the Netherlands
² The Hague University of Applied Science, Delft, the Netherlands
³ Department of the Built Environment, Eindhoven University of Technology, Eindhoven, the Netherlands

^* Corresponding author: ziao.wang@tudelft.nl

Abstract

This study investigates the diagnostic capabilities of a Diagnostic Bayesian Network (DBN) for air handling unit (AHU) components, particularly focusing on the heat recovery wheel (HRW) and heating coil valve (HCV). Unlike data-driven methods relying heavily on high-quality labeled data, this knowledge-based DBN is more suitable for real-world applications, where labeled faulty and normal data are hard to obtain. Notably, existing studies predominantly concentrate on developing DBN for AHU with recirculated air, neglecting thorough investigations into AHU with HRW, a prevalent system in North Europe and increasingly recommended post-COVID-19 for mitigating viral propagation. This paper presents a DBN setup with expert knowledge for an AHU with HRW, which is evaluated using experimental data from an office building in the Netherlands. The results show that the proposed DBN can successfully diagnose typical faults in HRW and HCV.