Inverse model-based detection of programming logic faults in multiple zone VAV AHU systems - Gunay, Burak; Bursill, Jayson; Huchuk, Brent; Shillinglaw, Scott (Building and environment, 2022)
Implementation of sequences of operation for heating, ventilation, and air conditioning (HVAC) systems into a building automation system (BAS) is a critical process for building performance. Mistakes are commonly made while interpreting the sequences of operation from the mechanical design, converting them into BAS programs, and making ad-hoc changes to the BAS programs to address occupant complaints. This paper presents a novel inverse model-based fault detection and diagnostics (FDD) method for the discovery of BAS programming logic faults in variable air volume (VAV) air handling unit (AHU) systems. Using widely available BAS trend data types, the method first trains inverse models characterizing the heat and air mass balance of a VAV AHU system’s mixing box, heating and cooling coils, VAV terminal units, and zones. Then, the expected operational behaviour of the VAV AHU system is constructed by using ASHRAE Guideline 36 sequences of operation and the trained inverse models. Deviations from the expected operation are treated as the symptoms for the detection of programming logic faults. The method is demonstrated with BAS trend data from a 41-zone VAV AHU system in Ottawa, Canada. Two faults in supply air temperature and pressure reset programs are detected, significantly increasing fan electricity and heating energy use. •An inverse model-based method is developed for detection of programming logic faults.•Inverse models are used to construct the expected operational behaviour.•Deviations from the expected operational behaviour are treated as faults.•The method is demonstrated with data from a 41-zone VAV AHU system.