Metode za otkrivanje i dijagnostiku kvarova u KGH sistemima
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Objavljeno
Feb 1, 2025
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Milica Kašiković
http://orcid.org/0000-0002-5142-1476
Aleksandar Anđelković
http://orcid.org/0000-0003-2736-6793
http://orcid.org/0000-0002-5142-1476
Aleksandar Anđelković
http://orcid.org/0000-0003-2736-6793
Apstrakt
Najveći deo potrošnje energije u zgradarstvu se troši u sistemima za klimatizaciju, grejanje i hlađenje. Efikasniji sistem, koji bi trošio manje energije se može postići blagovremenim otkrivanjem kvarova. Sve veća pažnja se posvećuje različitim metodama za otkrivanje i dijagnostiku kvarova sistema za klimatizaciju, grejanje i hlađenje zbog prihvatanja sistema za automatizaciju zgrada i unapređenja tehnika podataka i mašinskog učenja. Ove metode se mogu podeliti na metode zasnovane na modelu, znanju i podacima. U radu je dat pregled različitih metoda, a najveća se pažnja pridaje metodama zasnovanim na podacima zbog njihove velike tačnosti i bržeg i jeftinijeg razvoja modela.
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Kako citirati
KAŠIKOVIĆ, Milica; ANĐELKOVIĆ, Aleksandar.
Metode za otkrivanje i dijagnostiku kvarova u KGH sistemima.
Zbornik Međunarodnog kongresa o KGH, [S.l.], v. 55, n. 1, p. 75-83, feb. 2025.
Dostupno na: <https://izdanja.smeits.rs/index.php/kghk/article/view/8144>. Datum pristupa: 19 apr. 2025
doi: https://doi.org/10.24094/kghk.024.1.075.
Sekcija
Sistemi za KGH i oprema sistema za KGH
Reference
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[3] Granderson, J., G. Lin, A. Harding, P. Im, Y. Chen, Building fault detection data to aid diagnostic algorithm creation and performance testing, Sci. Data, vol. 7 (2020), no. 1, p. 65, https://doi:10.1038/s41597-020-0398-6
[4] Mirnaghi, M. S., F. Haghighat, Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings, (2020) https://doi.org/10.1016/j.enbuild.2020.110492
[5] Li, G., Y. Hu, J. Liu, X. Fang, J. Kang, Review on fault detection and diagnosis feature engineering in building heating, ventilation, air conditioning and refrigeration systems, IEEE Access 9, (2020) , 2153–2187
[6] Zhao, Y., T. Li, X. Zhang, C. Zhang, Artificial intelligence-based fault detection and diagnosis methods for building energy systems: advantages, challenges and the future, Renew. Sustain. Energy Rev. 109 (2019), 85–101, https://doi.org/10.1016/j.rser.2019.04.021
[7] Chen, J., L. Zhang, Y. Li, Y. Shi, X. Gao, Y. Hu, A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems, Renew. Sustain. Energy Rev. 161 (2022), https://doi.org/10.1016/j.rser.2022.112395
[8] Bi, J., H. Wang, E. Yan, C. Wang, K. Yan, L. Jiang, B. Yang, AI in HVAC fault detection and diagnosis: A systematic review, Energy Reviews, Volume 3 (2024), Issue 2, https://doi.org/10.1016/j.enrev.2024.100071
[9] Yang, H., T. Zhang, H. Li, D. Woradechjumroen, X. Liu, HVAC Equipment, Unitary: Fault Detection and Diagnosis, in Encyclopedia of Energy Engineering and Technology, Second Edi., no. November, Taylor & Francis, Ed. CRC Press (2014), pp. 854–864
[10] Katipamula, S., M. R. Brambley, Review Article : Methods for Fault Detection , Diagnostics, and Prognostics for Building Systems – A Review Part I, vol. 11 (2011), no. 1, pp. 3–25, https://doi:10.1080/10789669.2005.10391123
[11] Piscitelli, M. S., D. M. Mazzarelli, A. Capozzoli, Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules, Energy and Buildings, Volume 226 (2020), https://doi.org/10.1016/j.enbuild. 2020.110369
[12] Araya, D. B. An ensemble learning framework for anomaly detection in building energy consumption, Energy Build., vol. 144 (2017), pp. 191–206, https://doi:10.1016/j.enbuild.2017. 02.058
[13] Zhang, J., Y. Jiang, Bibliographical review on reconfigurable fault-tolerant control systems, in In Processing of the 5th IFAC Symposium on Fault Detection, Supervision and Safty for Technical Processes (2003)
[14] Yu, Y., D. Woradechjumroen, D. Yu, A review of fault detection and diagnosis methodologies on air-handling units, Energy Build., vol. 82 (2014), pp. 550–562, https://doi:10.1016/j.enbuild. 2014.06.042
[15] Ebrahimifakhar, A., A. Kabirikopaei, D. Yuill, Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods, Energy Build, vol. 225 (2020), p. 110318, https://doi.org/10.1016/j.enbuild.2020.110318
[16] Alzghoul, A., B. Backe, M. Lo, A. Bystro, B. Liljedahl, Computers in Industry Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection : A hydraulic drive system application, vol. 65 (2014), pp. 1126–1135, https://doi:10.1016/j.compind.2014.06.003
[17] Khorasgani, G., H. Farahat, A. Ristovski, K. Gupta, C. Biswas, A Framework for Unifying Model-based and Data-driven Fault Diagnosis, in Proceedings of the Annual Conference of the PHM Society (2018)
[18] Han, J., M. Kamber, J. Pei, Data Mining: Concepts and Techniques, Third edit. (2012)
[19] Yan, K., L. Ma, Y. Dai, W. Shen, Z. Ji, D. Xie, Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis, Int. J. Refrig., vol. 86 (2018), pp. 401–409, https://doi:10.1016/j.ijrefrig.2017.11.003
[20] Chandrashekar, G., F. Sahin, A survey on feature selection methods q, Comput. Electr. Eng., vol. 40 (2014), no. 1, pp. 16–28, https://doi:10.1016/j.compeleceng.2013.11.024
[21] Yan, K., W. Shen, T. Mulumba, A. Afshari, ARX model based fault detection and diagnosis for chillers using support vector machines, Energy Build., vol. 81 (2014), pp. 287–295, https://doi:10.1016/j.enbuild.2014.05.049
[22] Yan, K., C. Zhong, Z. Ji, J. Huang, Semi-supervised learning for early detection and diagnosis of various air handling unit faults, Energy & Buildings vol. 181 (2018), pp. 75–83, https://doi:10.1016/j.enbuild.2018.10.016
[23] Chen, Y., A. Ebrahimifakhar, Y. Hu, D. P. Yuill, Generalizability of machine learning-based fault classification for residential air-conditioners, Energy Build., Volume 295 (2023), https://doi.org/10.1016/j.enbuild.2023.113263
[24] Liu, Y., X. Hei, J. Zhao, Y. Zhang, G. Xie, Fault Diagnosis Based on Prior Knowledge for Train Air-Conditioning Unit, 11th International Conference on Computational Intelligence and Security (CIS), IEEE (2015), pp. 66–69.
[25] Sun, K., G. Li, H. Chen, J. Liu, J. Li, W. Hu, A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system's refrigerant charge fault amount, Appl. Therm. Eng. 108 (2016), 989–998, https://doi.org/10.1016/j.applthermaleng.2016.07.109
[26] Fan, Y., X. Cui, H. Han, H. Lu, Chiller Fault Detection and Diagnosis by Knowledge Transfer Based on Adaptive Imbalanced Processing. Science and Technology for the Built Environment, 26 (8) (2020), 1–23. https://doi:10.1080/23744731.2020.1757327
[27] Gharsellaoui, S., M. Mansouri, M. Trabelsi, M. F. Harkat, S. S. Refaat, H. Messaoud, Interval-valued features based machine learning technique for fault detection and diagnosis of uncertain HVAC systems, IEEE Access 8 (2020) 171892-171902. https://doi: 10.1109/ACCESS. 2020.3019365
[28] Borda, D., M. Bergagio, M. Amerio, M. C. Masoero, R. Borchiellini, D. Papurello, Development of Anomaly Detectors for HVAC Systems Using Machine Learning. Processes 11 (2023), 535. https://doi.org/10.3390/pr11020535
[29] Taher, S., A. Ahmadi, B. M. Ivatloo, S. Asadi, Fault detection diagnostic for HVAC systems via deep learning algorithms, Energy Buildings, Volume 250 (2021), https://doi.org/10.1016/j. enbuild.2021.111275
[30] Shahnazari, H., P. Mhaskar, J. M. House, T. I. Salsbury, Modeling and fault diagnosis design for HVAC systems using recurrent neural networks, Comput. Chem. Eng., vol. 126 (2019), pp. 189–203 https://doi:10.1016/j.compchemeng.2019.04.011
[31] Yu, Z., F. Haghighat, B. C. M. Fung, L. Zhou, A novel methodology for knowledge discovery through mining associations between building operational data, Energy Build., vol. 47 (2012), pp. 430–440 https://doi:10.1016/j.enbuild.2011.12.018
[32] Xue, P., Z. Zhou, X. Fang, X. Chen, L. Liu, Y. Liu, J. Liu, Fault detection and operation optimization in district heating substations based on data mining techniques, Appl. Energy, vol. 205 (2017), no. 73, pp. 926–940, https://doi:10.1016/j.apenergy.2017.08.03
[33] Du, Z., B. Fan, X. Jin, J. Chi, Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis, Build. Environ., vol. 73 (2014), pp. 1–11, https://doi:10.1016/j.buildenv.2013.11.021