%0 Journal Article
%K Fault detection and diagnostics (FDD)
%K Smart building
%K Fault correction
%K Field testing
%K Control hunting
%K Energy management and information system
%A Guanjing Lin
%A Marco Pritoni
%A Yimin Chen
%A Raphael Vitti
%A Christopher Weyandt
%A Jessica Granderson
%B Energy & Buildings
%D 2023
%G eng
%R https://doi.org/10.1016/j.enbuild.2023.112796
%T Implementation and test of an automated control hunting fault correction algorithm
%V Vol.283 No..112796
%8 01/2023
%X <p>Control hunting due to improper proportional–integral–derivative (PID) parameters in the building<br />automation system (BAS) is one of the most common faults identified in commercial buildings. It can<br />cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC)<br />equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest<br />growing market segments in smart building technologies in the United States. Implementation of PID<br />retuning procedures as an auto-correction algorithm and integration into FDD software has the<br />potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in<br />a scalable way. This paper presents the development, implementation, and field testing of an<br />automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The<br />algorithm was developed in a commercial FDD software and successfully tested among nine variable air<br />volume boxes in an office building in the United States. The paper shows the feasibility of using FDD<br />tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a<br />path forward for the HVAC industry and academia to further improve this technology.</p>