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Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Author

Listed:
  • Simon P. Melgaard

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Ø, Denmark)

  • Kamilla H. Andersen

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Ø, Denmark)

  • Anna Marszal-Pomianowska

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Ø, Denmark)

  • Rasmus L. Jensen

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Ø, Denmark)

  • Per K. Heiselberg

    (Department of the Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Ø, Denmark)

Abstract

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

Suggested Citation

  • Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4366-:d:839459
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    References listed on IDEAS

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    1. Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
    2. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    3. Wang, Shengwei & Cui, Jingtan, 2005. "Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method," Applied Energy, Elsevier, vol. 82(3), pages 197-213, November.
    4. Michael Parzinger & Lucia Hanfstaengl & Ferdinand Sigg & Uli Spindler & Ulrich Wellisch & Markus Wirnsberger, 2020. "Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems," Sustainability, MDPI, vol. 12(17), pages 1-18, August.
    5. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    6. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    7. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    8. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
    9. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    10. Li, Bingxu & Cheng, Fanyong & Zhang, Xin & Cui, Can & Cai, Wenjian, 2021. "A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data," Applied Energy, Elsevier, vol. 285(C).
    11. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    12. Bonvini, Marco & Sohn, Michael D. & Granderson, Jessica & Wetter, Michael & Piette, Mary Ann, 2014. "Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques," Applied Energy, Elsevier, vol. 124(C), pages 156-166.
    13. Ting Hu & Zhikun Ding, 2021. "An Integrated Prediction Model for Building Energy Consumption: A Case Study," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1655-1665, Springer.
    14. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    15. Gao, Dian-ce & Wang, Shengwei & Shan, Kui & Yan, Chengchu, 2016. "A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems," Applied Energy, Elsevier, vol. 164(C), pages 1028-1038.
    16. Najafi, Massieh & Auslander, David M. & Bartlett, Peter L. & Haves, Philip & Sohn, Michael D., 2012. "Application of machine learning in the fault diagnostics of air handling units," Applied Energy, Elsevier, vol. 96(C), pages 347-358.
    17. Ardeshir Mahdavi & Christiane Berger & Hadeer Amin & Eleni Ampatzi & Rune Korsholm Andersen & Elie Azar & Verena M. Barthelmes & Matteo Favero & Jakob Hahn & Dolaana Khovalyg & Henrik N. Knudsen & Ale, 2021. "The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?," Sustainability, MDPI, vol. 13(6), pages 1-44, March.
    18. Liu, Jiangyan & Li, Guannan & Liu, Bin & Li, Kuining & Chen, Huanxin, 2019. "Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system," Energy, Elsevier, vol. 174(C), pages 873-885.
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    Cited by:

    1. Antonio Rosato & Marco Savino Piscitelli & Alfonso Capozzoli, 2023. "Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings," Energies, MDPI, vol. 16(2), pages 1-6, January.

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