IN-24-037 – A Co-Simulation Based Study of Smart Thermostats’ Potential for Fault Detection and Diagnosis Using Duty Cycle

Due to the prospect of reducing the sensor requirements for fault detection and diagnosis (FDD) in residential air-conditioning (AC) systems by using smart thermostat data, there has been growing research interests on smart thermostat based FDD. However, smart thermostat data is a lumped representation of indoor air responses to both AC operations and other factors like weather and building gains. While estimating uncontrollable building gain disturbances is essential to differentiate them from AC impacts, smart thermostat data are insufficient for estimating these gains. Thus, in this study, the capacity of smart thermostat data-driven FDD is investigated by using an integrated building and Vapor Compression cycle-based AC model developed and simulated using EnergyPlus/Spawn and Dymola. The simulation was performed under ten scenarios covering relevant gains, low charge, and low indoor airflow faults. Since duty factor has been adopted as an effective index to detect faults for most smart thermostat FDD methods, the sensitivity of the duty factor feature under these scenarios was studied. The results show duty factor increase of about 8% from varying building gains. Low charge of 10%, 20%, and 30% severities gave duty factor rise of about 4%, 10% and 20% respectively. While low indoor airflow at 10%, 20%, and 30% severities gave duty factor rise of about 4%, 10% and 20%, respectively. The results suggest that if these gains are neglected in an FDD method, it might not be possible to detect either fault at severities below 30%. Thus, this study demonstrates the need to consider some of these gains in developing smart thermostat based FDD algorithms if the detection of less severe faults is desired.