Fault prioritisation for Air Handling units using fault modelling and actual fault occurrence data

Fault impact analysis is an important step in developing Fault Detection and Diagnosis (FDD) tools for Heating, Ventilation and Air-Conditioning (HVAC) systems. It provides an insight into how HVAC systems react in the presence of operational faults and helps to prioritise which faults to focus on. The research in the existing literature has mostly focused on modelling several faults occurring in various HVAC systems and components to evaluate the effect of the fault on energy performance and the predicted thermal comfort of occupants. However, the real frequency of fault occurrence, an important factor which affects the overall impact a fault has on the system's performance, has barely been addressed in the reviewed literature. This paper addresses this gap by considering real fault occurrence frequency data in addition to the effect of the faults on the energy performance of the building to assess their total energy impact. A real office building in the Netherlands was modelled using DesignBuilder and its energy performance was simulated using EnergyPlus. Air Handling Unit (AHU) faults were introduced using the native fault object and Energy Management System (EMS) features available in EnergyPlus. The fault occurrence data was extracted from AHU work orders using text mining. The fault modelling and text mining results were combined to get the total energy impact of the fault and, subsequently, the faults were prioritised using the Pareto principle. The research identified that fan failure, Heat Recovery Wheel (HRW) failure, fan stuck at 50% and Heating Coil Valve (HCV) stuck at 0% are the priority faults for winter and fan failure is the priority fault for summer.