Share:


Analysis of equipment faults in indoor climate systems and their detection and diagnosis measures

Abstract

Indoor climate systems required to provide indoor climate and ensure indoor air quality, failures affect the amount of energy consumed in a building, although insufficient attention is paid to their operation. The energy consumption can be reduced due to ensured proper operation of indoor climate systems avoiding equipment faults. The article reviews scientific articles, those represent typical heating, ventilation and air conditioning (HVAC) systems equipment faults of the most energy intensive office and commercial buildings. Measures of detecting and diagnosis equipment failures as well are identified. A generalized overview of the study area shows the typical faults of the indoor climate system are related to the control of the devices, sensors, deterioration of equipment performance. The most commonly used methods for detecting and diagnosing faults are automated fault detection and diagnosis (AFDD) methods. Possible solutions for troubleshooting HVAC systems are presented.


Article in Lithuanian.


Mikroklimato sistemų įrangos gedimų ir jų nustatymo bei diagnozavimo priemonių analizė


Santrauka


Mikroklimato sistemų (MKS), pastate sukuriančių mikroklimatą ir užtikrinančių gerą oro kokybę, gedimai turi įtakos pastate suvartojamam energijos kiekiui, nors sistemas eksploatuojant tam skiriama nepakankamai dėmesio. Užtikrinant tinkamą MKS veikimą, siekiant išvengti įrangos gedimų, galima sumažinti jose suvartojamos energijos kiekį. Apžvelgus mokslines publikacijas, straipsnyje pateikiami charakteringi biurų ir prekybos pastatų, kaip imliausių energijai šildymo, vėdinimo ir oro kondicionavimo (ŠVOK) sistemų įrangos gedimai. Taip pat įvardijamos įrangos gedimų nustatymo ir diagnozavimo priemonės. Apibendrinta tiriamos srities apžvalga rodo, kad MKS būdingi gedimai susiję su įrangos valdymu, jutikliais, įrangos eksploatacinių savybių blogėjimu. Dažniausiai gedimams nustatyti ir diagnozuoti taikomi automatizuoti gedimų aptikimo ir diagnozavimo metodai (AGAD). Pateikiami galimi sprendimai gedimams ŠVOK sistemose šalinti.


Reikšminiai žodžiai: automatizuotas gedimų aptikimas ir diagnozavimas, inžinerinės sistemos, įrangos gedimai, jutikliai, mikroklimato sistemos.

Keyword : automated fault detection and diagnosis, engineering systems, equipment faults, sensors, indoor climate systems

How to Cite
Misevičiūtė, V. (2020). Analysis of equipment faults in indoor climate systems and their detection and diagnosis measures. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 12. https://doi.org/10.3846/mla.2020.13086
Published in Issue
Sep 29, 2020
Abstract Views
644
PDF Downloads
486
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Al-Waked, R., Shakir Nasif, M., Groenhout, N., & Partridge, L. (2017). Energy performance and CO2 emissions of HVAC Systems in commercial buildings. Buildings, 7(4), 84. https://doi.org/10.3390/buildings7040084

Basarkar, M., Pang, X., Wang, L., Haves, P., & Hong, T. (2011, November 14−16). Modeling and simulation of HVAC results in EnergyPlus. Paper presented at the 12th Conference of International Building Performance Simulation Association, Sydney.

Chakraborty, D., & Elzarka, H. (2019). Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold. Energy & Buildings, 185, 326–344.
https://doi.org/10.1016/j.enbuild.2018.12.032

Cheung, H., Braun, J. E., Technical, N., Stephen, M., Cheung, H., & Braun, J. E. (2015). Development of fault models for hybrid fault detection and diagnostics algorithm: October 1, 2014 -- May 5, 2015 (Technical report). United States. https://doi.org/10.2172/1235409

Cotts, D. G., Roper, K. O., & Payant, R. P. (2009). The facility management handbook (3rd ed.). AMACOM.

Homod, R. Z., Gaeid, K. S., Dawood, S. M., Hatami, A., & Sahari, K. S. (2020). Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings. Applied Energy, 271, 115255.
https://doi.org/10.1016/j.apenergy.2020.115255

Katipamula, S., & Brambley, M. R. (2005). Methods for fault detection, diagnostics, and prognostics for building systems—a review, Part I. HVAC&R RESEARCH, 11(1), 3–25. https://doi.org/10.1080/10789669.2005.10391123

Kim, W., & Katipamula, S. (2018). A review of fault detection and diagnostics methods for building systems. Science and Technology for the Built Environment, 24(1), 3–21.
https://doi.org/10.1080/23744731.2017.1318008

Papadopoulos, S., Kontokosta, C. E., Vlachokostas, A., & Azar, E. (2019). Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates. Building and Environment, 155, 350–359. https://doi.org/10.1016/j.buildenv.2019.03.062

Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Building, 40(3), 394–398. https://doi.org/10.1016/j.enbuild.2007.03.007

Piette, M. A., Kinney, S. K., & Haves, P. (2001). Analysis of an information monitoring and diagnostic system to improve building operations. Energy and Buildings, 33(8), 783–791. https://doi.org/10.1016/S0378-7788(01)00068-8

Roth, K. W., Llana, P., & Feng, M. (2004). The energy impact of faults in U.S. commercial buildings. Paper presented at the International Refrigeration and Air Conditioning Conference.

Shahnazari, H., Mhaskar, P., House, J. M., & Salsbury, T. I. (2019). Modeling and fault diagnosis design for HVAC systems using recurrent neural networks. Computers & Chemical Engineering, 126, 189–203.
https://doi.org/10.1016/j.compchemeng.2019.04.011

Taal, A., Itard, L., & Zeiler, W. (2018). A reference architecture for the integration of automated energy performance fault diagnosis into HVAC systems. Energy & Buildings, 179, 144–155. https://doi.org/10.1016/j.enbuild.2018.08.031

Tian, Z., Si, B., Shi, X., & Fang, Z. (2019). An application of Bayesian Network approach for selecting energy efficient HVAC systems. Journal of Building Engineering, 25, 100796. https://doi.org/10.1016/j.jobe.2019.100796

Turner, W. J. N., Staino, A., & Basu, B. (2017). Residential HVAC fault detection using a system identification approach. Energy & Buildings, 151, 1–17. https://doi.org/10.1016/j.enbuild.2017.06.008

Verbert, K., Babu, R., & De Schutter, B. (2017). Combining knowledge and historical data for system-level fault diagnosis of HVAC systems. Engineering Applications of Artificial Intelligence, 59, 260–273.
https://doi.org/10.1016/j.engappai.2016.12.021

Visier, J. C., Li, X., Coralles, P., Irigoin, M., Le Vannier, I., Lovetri, J., Le Men, M., & Picard, P. (1997). Fault detection and diagnosis tool for schools heating systems (pp. 1–15). In Clima 2000. Brussels, Belgium.

Wang, H., Chen, Y., Chan, C. W. H., Qin, J., & Wang, J. (2012). Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy & Buildings, 55, 252–263. https://doi.org/10.1016/j.enbuild.2012.08.016

Wang, S., & Wang, J. (1999). Law-based sensor fault diagnosis and validation for building air-conditioning systems.
HVAC&R Research, 5(4), 353–380. https://doi.org/10.1080/10789669.1999.10391243

Yang, C., Shen, W., Chen, Q., & Gunay, B. (2018). A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance. Journal of Building Engeering, 15, 26–32. https://doi.org/10.1016/j.jobe.2017.10.013

Yoshida, Y. (2006). Development of air conditioning technologies to reduce CO2 emissions in the commercial sector. Carbon Balance and Management, 1–12. https://doi.org/10.1186/1750-0680-1-12

Zhang, R., & Hong, T. (2017). Modeling of HVAC operational faults in building performance simulation. Applied Energy, 202, 178–188. https://doi.org/10.1016/j.apenergy.2017.05.153

Zhao, Y., Li, T., Fan, C., Lu, J., Zhang, X., Zhang, C., & Chen, S. (2019). A proactive fault detection and diagnosis method for variable-air-volume terminals in building air conditioning systems. Energy & Buildings, 183, 527–537. https://doi.org/10.1016/j.enbuild.2018.11.021