Implementation and test of an automated control hunting fault correction algorithm

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building
automation system (BAS) is one of the most common faults identified in commercial buildings. It can
cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC)
equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest
growing market segments in smart building technologies in the United States. Implementation of PID
retuning procedures as an auto-correction algorithm and integration into FDD software has the
potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in
a scalable way. This paper presents the development, implementation, and field testing of an
automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The
algorithm was developed in a commercial FDD software and successfully tested among nine variable air
volume boxes in an office building in the United States. The paper shows the feasibility of using FDD
tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a
path forward for the HVAC industry and academia to further improve this technology.