Investigation of an IoT-Based Approach for Automated Fault Detection in Residential HVAC Systems

摘要

Automated fault detection and diagnosis (AFDD) in residential heating, ventilation and air-conditioning (HVAC) systems has received lots of attention in recent times particularly with the advent of smart thermostat technology which has encouraged research into cost-efficient ways of performing AFDD without installation of additional sensors. However, investigation of some of the proposed smart thermostat-based methods using field data have revealed that smart thermostat-based data alone are not sufficient for a reliable AFDD. Most of these studies are based on simulation data rather than field data which have more disturbances. Moreover, the simplifications made in some of the studies further reveal the challenge which these AFDD approach would have if implemented in real life systems. Therefore, a more promising approach, i.e., an IoT-based approach that involves additional smart devices, is proposed in this study. The proposed approach makes use of a simple model, which was trained using outdoor dry-bulb temperature and indoor wet-bulb temperature. The trained model is then used to predict the enthalpy change across the evaporator of a residential HVAC system. The predicted enthalpy difference is next compared with actual enthalpy difference computed using temperature and relative humidity measurements from the return and supply side of the HVAC system and deviations between the two are used to detect presence of fault in the system. An experimental test was carried out in a test house located in Norman, Oklahoma. The results obtained show that the proposed AFDD algorithm was able to successfully detect low indoor airflow faults even with the use of measured data rather than simulation data as used in previous studies. Though unlike the thermostat-based approach, the proposed IoT-based approach requires one additional sensor, which often comes with smart thermostat purchase for additional room temperature measuerment. Hence with the placement of the smart sensor in one of the closest air difffusers, the studied AFDD can be done on a residential HVAC and cost savings can be realised for homeowners using our proposed IoT-based approach.