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Data-driven fault detection and diagnosis for centralised chilled water air conditioning system

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Wykrywanie i diagnostyka usterek w oparciu o dane dla scentralizowanego systemu klimatyzacji na wodę lodową
Języki publikacji
EN
Abstrakty
EN
The air conditioning system is complex and consumes the most energy in the building. Due to its complexity, it is difficult to identify faults in the system immediately. In this project, fault detection and diagnosis system using decision tree classifier model was developed to detect and diagnose faults in a chilled water air conditioning system. The developed model successfully classified normal condition and five common faults for more than 99% accuracy and precision. A graphical user interface of the system was also developed to ease the users.
PL
System klimatyzacji jest złożony i zużywa najwięcej energii w budynku. Ze względu na swoją złożoność trudno jest od razu zidentyfikować usterki w systemie. W ramach tego projektu opracowano system wykrywania i diagnostyki usterek wykorzystujący model klasyfikatora drzewa decyzyjnego do wykrywania i diagnozowania usterek w systemie klimatyzacji wody lodowej. Opracowany model pomyślnie sklasyfikował stan normalny i pięć typowych usterek, zapewniając ponad 99% dokładności i precyzji. W celu ułatwienia użytkownikom opracowano również graficzny interfejs użytkownika systemu.
Rocznik
Strony
217--221
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
  • Centre of Electrical Energy Systems (CEES), School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia
Bibliografia
  • [1] C. G. Mattera, J. Quevedo, T. Escobet, H. R. Shaker, and M. Jradi, "A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors," Sensors (Basel)., vol. 18, no. 11, pp. 1–21, 2018.
  • [2] J. Kim, J. Cai, and J. E. Braun, "Common Faults and Their Prioritization in Small Commercial Buildings Common Faults and Their Prioritization in Small Commercial Buildings," Golden, CO: National Renewable Energy Laboratory, 2018.
  • [3] M. F. Othman, H. Abdullah, N. A. Sulaiman, and M. Y. Hassan, "Performance evaluation of an actual building air-conditioning system," in IOP Conference Series: Materials Science and Engineering, 2013, vol. 50, no. 1.
  • [4] A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo, and F. Simmini, “Data-driven Fault Detection and Diagnosis for HVAC water chillers,” Control Eng. Pract., vol. 53, pp. 79–91, 2016.
  • [5] W. Kim and S. Katipamula, "A review of fault detection and diagnostics methods for building systems," Sci. Technol. Built Environ., vol. 24, no. 1, pp. 3–21, 2018.
  • [6] Z. Li et al., "An Effective Fault Detection and Diagnosis Approach for Chiller System," IFAC-PapersOnLine, vol. 52, no. 10, pp. 55–60, 2019.
  • [7] M. E. S. Trothe, H. R. Shaker, M. Jradi, and K. Arendt, "Fault isolability analysis and optimal sensor placement for fault diagnosis in smart buildings," Energies, vol. 12, no. 9, 2019.
  • [8] E. K. Alexandersen, M. R. Skydt, S. S. Engelsgaard, M. Bang, M. Jradi, and H. R. Shaker, "A stair-step probabilistic approach for automatic anomaly detection in building ventilation system operation," Build. Environ., vol. 157, no. February, pp. 165– 171, Jun. 2019.
  • [9] A. Beghi, L. Cecchinato, F. Peterle, M. Rampazzo, and F. Simmini, “Model-based fault detection and diagnosis for centrifugal chillers,” Conf. Control Fault-Tolerant Syst. SysTol, vol. 2016-Novem, pp. 158–163, 2016.
  • [10] Y. Li and Z. O'Neill, "A critical review of fault modeling of HVAC systems in buildings," Build. Simul., vol. 11, no. 5, pp. 953–975, 2018.
  • [11] F. Lauro et al., "Building fan coil electric consumption analysis with fuzzy approaches for fault detection and diagnosis," Energy Procedia, vol. 62, no. June, pp. 411–420, 2014.
  • [12] P. S. Pouabe Eboule and A. N. Hasan, "Accurate fault detection and location in power transmission line using concurrent neuro fuzzy technique," Prz. Elektrotechniczny, vol. 97, no. 1, pp. 37–45, 2021.
  • [13] N. A. Sulaiman, M. F. Othman, and H. Abdullah, "Fuzzy logic control and fault detection in centralized chilled water system," Proc. - 2015 IEEE Symp. Ser. Comput. Intell. SSCI 2015, pp. 8–13, 2015.
  • [14] C. G. Mattera, M. Jradi, M. R. Skydt, S. S. Engelsgaard, and H. R. Shaker, "Fault detection in ventilation units using dynamic energy performance models," J. Build. Eng., vol. 32, 2020.
  • [15] S. Deshmukh, S. Samouhos, L. Glicksman, and L. Norford, "Fault detection in commercial building VAV AHU: A case study of an academic building," Energy Build., vol. 201, pp. 163–173, 2019.
  • [16] B. Li, F. Cheng, X. Zhang, C. Cui, and W. Cai, "A Novel Semisupervised Data-driven Method for Chiller Fault Diagnosis with Unlabeled Data," Appl. Energy, vol. 285, pp. 1–13, 2021.
  • [17] Y. Fan, X. Cui, H. Han, and H. Lu, "Chiller fault diagnosis with field sensors using the technology of imbalanced data," Appl. Therm. Eng., vol. 159, no. June, 2019.
  • [18] X. J. Luo, K. F. Fong, Y. J. Sun, and M. K. H. Leung, "Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system," Energy Build., vol. 186, pp. 17–36, 2019.
  • [19] W. S. Yun, W. H. Hong, and H. Seo, "A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states," J. Build. Eng., vol. 35, 2021.
  • [20] M. S. Piscitelli, D. M. Mazzarelli, and A. Capozzoli, "Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules," Energy Build., vol. 226, 2020.
  • [21] K. Yan, J. Huang, W. Shen, and Z. Ji, "Unsupervised learning for fault detection and diagnosis of air handling units," Energy Build., vol. 210, p. 109689, 2020.
  • [22] J. Li, Y. Guo, J. Wall, and S. West, "Support vector machine based fault detection and diagnosis for HVAC systems," Int. J. Intell. Syst. Technol. Appl., vol. 18, no. 1–2, pp. 204–222, 2019.
  • [23] N. A. Sulaiman, P. Abdullah, H. Abdullah, M. N. S. Zainuddin, and A. Md Yusop, "Fault detection for air conditioning system using machine learning," IAES Int. J. Artif. Intell., vol. 9, no. 1, pp. 109–116, 2020.
  • [24] R. Benkercha and S. Moulahoum, "Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system," Sol. Energy, vol. 173, no. July, pp. 610–634, 2018.
  • [25] S. H. . Asman, N. F. . Ab Aziz, U. A. . Ungku Amirulddin, and M. Z. A. Ab Kadir, "Decision Tree Method for Fault Causes Classification Based on RMS-DWT Analysis in 275 kV Transmission Lines Network," Appl. Sci., vol. 11, no. 4031, 2021.
  • [26] C. K. Madhusudana, H. Kumar, and S. Narendranath, "Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal," Mater. Today Proc., vol. 5, no. 5, pp. 12035–12044, 2018.
  • [27] M. Golmoradi, E. Ebrahimi, and M. Javidan, "Fault diagnosis of compressor based on decision tree and fuzzy inference system," Vibroengineering Procedia, vol. 12, pp. 54–60, 2017.
  • [28] M. S. Mirnaghi and F. Haghighat, "Fault detection and diagnosis of large-scale HVAC systems in buildings using datadriven methods: A comprehensive review," Energy Build., vol. 229, p. 110492, 2020.
  • [29] A. Contreras-Valdes, J. P. Amezquita-Sanchez, D. Granados- Lieberman, and M. Valtierra-Rodriguez, “Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review,” Appl. Sci., vol. 10, no. 950, 2020.
  • [30] V. Balasubramaniam, "Fault Detection and Diagnosis in Air Handling Units with a Novel Integrated Decision Tree Algorithm," J. Trends Comput. Sci. Smart Technol., vol. 3, no. 1, pp. 49–58, 2021.
  • [31] G. Li et al., "An improved decision tree-based fault diagnosis method for practical variable refrigerant flow system using virtual sensor-based fault indicators," Appl. Therm. Eng., vol. 129, pp. 1292–1303, 2018.
  • [32] N. A. Sulaiman, M. F. Othman, and H. Abdullah, "Fuzzy logic control of centralized chilled water system," J. Teknol., vol. 72, no. 2, pp. 57–62, 2015.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-43e97ac8-9362-4f7c-a839-2d8fbfee577c
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