Overview on Fault Detection and Diagnosis Methods in Building HVAC Systems: Toward a Hybrid Approach

This paper aims to provide a summarized classification of fault detection and diagnosis (FDD) methods in Heating Ventilation and Air Conditioning (HVAC) systems by dividing them into knowledge-driven-based, data-driven and hybrid approaches, and then subdividing each category to more detailed categories. Considering the advantages and disadvantages of each method, it is concluded that knowledge-driven approaches require noticeable expertise, high number of input variables and consequently sensors to be installed, also having scalability issues. On the other hand, data-driven methods provide more precise results, while they require reliable labeled fault free and/or faulty data which is hard to access especially in real-world Building Automation System (BAS) data. Considering the disadvantages of knowledge-based and data-driven approaches and following a brief explanation of current studies based on hybrid methods, this paper highlights the necessity of hybrid FDD approach expansion in the future studies specifically in fault diagnosis.