Data Resource Library for AHU of HVAC/ACMV Condition Monitoring: A Case Study Using AI-Driven Intelligence Approach

Abstract

Air Handling Units (AHU) classic leakage of outdoor-air dampers generates energy 
increments in the cooling-system and this energy increments is used for threshold-based Fault 
Detection and Diagnosis (FDD). For tropical buildings equipped with gas-fired reheats the 
thermodynamic effect on heating and reheat load resulting from excess outdoor air enthalpy leads 
to loss of such characteristic energy signatures, making it impossible for FDD systems based on 
fan electric power to identify the fault. This work generates a public-accessible labeled data 
library on a particular type of fault occurring in a tropical climate gas-reheat VAV AHU. 
EnergyPlus 23.2 and OpenStudio 3.7.0 simulation, with modifications of the ORNL Flexible 
Research Platform for Kuala Lumpur climate and with fault levels of severity between 20% and 
50%, yield 16,680 hours analyzed based on Fault Impact Ratio calculation, sensitivity ranking, 
and Random Forest classification. The fault manifests as an ambiguous signal pattern: no fan 
electricity sensitivity (0.00%), 2.5% growth in cooling coil electricity consumption, up to 85.8% 
reduction in hot water pump electricity, and up to 88.66% decrease in boiler natural gas 
consumption, rendering both fan-based and energy threshold FDD ineffective. Electrical-signal
based FDD attains 71.47% balanced accuracy while employing the practical signal group yields 
95.49%. This dataset is introduced as an open access FDD  dataset for Southeast Asia. Thermal 
and latent energy signatures of the hot water loop and cooling coil, respectively, can be used to 
diagnose the fault without any direct airflow measurements.