175
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

ORCID Icon, , ORCID Icon, &
Pages 887-904 | Received 25 Jan 2023, Accepted 16 Jun 2023, Published online: 02 Aug 2023

References

  • Bellanco, I., E. Fuentes, M. Valles, and J. Salom. 2021. A review of the fault behavior of heat pumps and measurements, detection and diagnosis methods including virtual sensors. Journal of Building Engineering 39:102254. 10.1016/j.jobe.2021.102254.
  • Butzbaugh, J., A. Tidwell, and C. Antonopoulos. 2020. Automated fault detection & diagnostics: Residential market analysis (Publication No. PNNL-30077). Pacific Northwest National Laboratory.
  • Cericola, R. 2015. Emerson ComfortGuard HVAC monitoring service can predict problems. Emerson Electric. https://www.electronichouse.com/home-energy-management/emerson-comfortguard-hvac-monitoring-service-can-predict-problems/
  • Cetin, K. S., and C. Kallus. 2016. Data-driven methodology for energy and peak load reduction of residential HVAC systems. Procedia Engineering 145:852–9. 10.1016/j.proeng.2016.04.205.
  • Chen, B., and J. E. Braun. 2000. Simple fault detection and diagnosis methods for packaged air conditioners. In International Refrigeration and Air Conditioning Conference. Paper 498. http://docs.lib.purdue.edu/iracc/498
  • Chintala, R., J. Winkler, and X. Jin. 2021. Automated fault detection of residential air-conditioning systems using thermostat drive cycles. Energy and Buildings 236:110691. 10.1016/j.enbuild.2020.110691.
  • Cutler, D., J. Winkler, N. Kruis, C. Christensen, and M. Brandemuehl. 2013. Improved modeling of residential air conditioners and heat pumps for energy calculations (Issue January). National Renewable Energy Laboratory (NREL), U.S. Department of Energy.
  • Ejenakevwe, K. A., and L. Song. 2021. Review of fault detection and diagnosis studies on residential HVAC systems. Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021, 1–14.
  • Ejenakevwe, K., J. Wang, and L. Song. 2022. Investigation of an IoT-based approach for automated fault detection in residential HVAC systems. ASHRAE Transactions 128 (2):219–28.
  • Emerson, E. 2017. ComfortAlert for residential applications. Emerson Electric. https://climate.emerson.com/en-us/shop/1/copeland-comfortalert-for-residential-applications
  • Emerson, E. 2021. Sensi predict smart HVAC for contractors. Emerson Electric. https://climate.emerson.com/en-us/brands/sensi/sensi-predict-for-contractors
  • Grigg, O. A., V. T. Farewell, and D. J. Spiegelhalter. 2003. Use of risk-adjusted CUSUM and RSPRT charts for monitoring in medical contexts. Statistical Methods in Medical Research 12 (2):147–70. 10.1177/096228020301200205.
  • Guo, F., A. P. Rogers, and B. P. Rasmussen. 2022a. Multivariate fault detection for residential HVAC systems using cloud-based thermostat data, part I: Methodology. Science and Technology for the Built Environment 28 (2):109–20. 10.1080/23744731.2021.2005375.
  • Guo, F., A. P. Rogers, and B. P. Rasmussen. 2022b. Multivariate fault detection for residential HVAC systems using cloud-based thermostat data, part II: Case studies. Science and Technology for the Built Environment 28 (2):121–36. 10.1080/23744731.2021.1987141.
  • Guo, Y., Z. Tan, H. Chen, G. Li, J. Wang, R. Huang, J. Liu, and T. Ahmad. 2018. Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving. Applied Energy 225:732–45. 10.1016/j.apenergy.2018.05.075.
  • Jain, M., M. Gupta, A. Singh, and V. Chandan. 2019. Beyond control: Enabling smart thermostats for leakage detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3 (1):1–21. 10.1145/3314401.
  • Katipamula, S., and M. R. Brambley. 2005. Methods for fault detection, diagnostics, and prognostics for building systems—A review, Part I. HVAC&R Research 11 (1):3–25. 10.1080/10789669.2005.10391133.
  • Kim, W., and J. E. Braun. 2016. Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics. International Journal of Refrigeration 63:184–98. 10.1016/J.IJREFRIG.2015.11.005.
  • Kim, W., J. E. Braun, M. Engineering, and W. Lafayette. 2012. Virtual refrigerant mass flow and power sensors for variable-speed compressors. International Refrigeration and Air Conditioning Conference, 1–8.
  • Li, H., and J. E. Braun. 2007. Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners. International Journal of Refrigeration 30 (3):546–64. 10.1016/j.ijrefrig.2006.07.024.
  • Li, H., and J. E. Braun. 2009a. Virtual refrigerant pressure sensors for use in monitoring and fault diagnosis of Vapor-Compression equipment. HVAC&R Research 15 (3):597–616. 10.1080/10789669.2009.10390853.
  • Li, H., and J. E. Braun. 2009b. Development, evaluation, and demonstration of a virtual refrigerant charge sensor. HVAC&R Research 15 (1):117–36. 10.1080/10789669.2009.10390828.
  • Rogers, A., F. Guo, and B. Rasmussen. 2020. Uncertainty analysis and field implementation of a fault detection method for residential HVAC systems. Science and Technology for the Built Environment 26 (3):320–33. 10.1080/23744731.2019.1676093.
  • Rogers, A. P., F. Guo, and B. P. Rasmussen. 2019. A review of fault detection and diagnosis methods for residential air conditioning systems. Building and Environment 161:106236. 10.1016/j.buildenv.2019.106236.
  • Song, L., G. Wang, and M. R. Brambley. 2013. Uncertainty analysis for a virtual flow meter using an air-handling unit chilled water valve. HVAC and R Research 19 (3):335–45. 10.1080/10789669.2013.774890.
  • Sun, Z., H. Jin, J. Gu, Y. Huang, X. Wang, and X. Shen. 2019. Gradual fault early stage diagnosis for air source heat pump system using deep learning techniques. International Journal of Refrigeration 107:63–72. 10.1016/j.ijrefrig.2019.07.020.
  • Turner, W. J. N., A. Staino, and B. Basu. 2017. Residential HVAC fault detection using a system identification approach. Energy and Buildings 151:1–17. 10.1016/j.enbuild.2017.06.008.
  • Van Der Ham, W., M. Klein, S. A. Tabatabaei, D. J. Thilakarathne, and J. Treur. 2016. Methods for a smart thermostat to estimate the characteristics of a house based on sensor data. Energy Procedia 95:467–74. 10.1016/j.egypro.2016.09.067.
  • Wang, J., C. Y. Tang, and L. Song. 2020. Design and analysis of optimal pre-cooling in residential buildings. Energy and Buildings 216:109951. 10.1016/j.enbuild.2020.109951.
  • Yoo, J. W., S. B. Hong, and M. S. Kim. 2017. Refrigerant leakage detection in residential air conditioner with limited sensor installations. International Journal of Refrigeration 78:157–65. 10.1016/j.ijrefrig.2017.03.001.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.