TO-22-C025 – Investigation of an IoT-Based Approach for Automated Fault Detection in Residential HVAC Systems
Automated fault detection and diagnosis (AFDD) in residential heating, ventilation and air-conditioning (HVAC) systems has received lots of attentionin recent times particularly with the advent of smart thermostat technology which has encouraged research into cost-efficient ways of performing AFDDwithout installation of additional sensors. However, investigation of some of the proposed smart thermostat-based methods using field data have revealedthat smart thermostat-based data alone are not sufficient for a reliable AFDD. Most of these studies are based on simulation data rather than field datawhich have more disturbances. Moreover, the simplifications made in some of the studies further reveal the challenge which these AFDD approach wouldhave if implemented in real life systems. Therefore, a more promising approach, i.e., an IoT-based approach that involves additional smart devices, isproposed 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 trainedmodel is then used to predict the enthalpy change across the evaporator of a residential HVAC system. The predicted enthalpy difference is next comparedwith actual enthalpy difference computed using temperature and relative humidity measurements from the return and supply side of the HVAC system anddeviations 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 wasable to successfully detect low indoor airflow faults even with the use of measured data rather than simulation data as used in previous studies. Thoughunlike the thermostat-based approach, the proposed IoT-based approach requires one additional sensor, which often comes with smart thermostat purchasefor additional room temperature measurement. Hence with the placement of the smart sensor in one of the closest air diffusers, the studied AFDD can bedone on a residential HVAC and cost savings can be realized for homeowners using our proposed IoT-based approach.
Product Details
- Published:
- 2022
- Number of Pages:
- 10
- Units of Measure:
- Dual
- File Size:
- 1 file , 2.2 MB
- Product Code(s):
- D-TO-22-C025
- Note:
- This product is unavailable in Russia, Belarus
