IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5534-d876157.html
   My bibliography  Save this article

Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review

Author

Listed:
  • William Nelson

    (Department of Mechanical Engineering, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA)

  • Charles Culp

    (Department of Architecture, Energy Systems Laboratory, Texas AM University, College Station, TX 78412, USA)

Abstract

Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.

Suggested Citation

  • William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5534-:d:876157
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5534/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/15/5534/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    3. 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).
    4. 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.
    5. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    6. 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.
    7. Gaitani, N. & Lehmann, C. & Santamouris, M. & Mihalakakou, G. & Patargias, P., 2010. "Using principal component and cluster analysis in the heating evaluation of the school building sector," Applied Energy, Elsevier, vol. 87(6), pages 2079-2086, June.
    8. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    2. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. 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.
    3. Ren, Haoshan & Xu, Chengliang & Lyu, Yuanli & Ma, Zhenjun & Sun, Yongjun, 2023. "A thermodynamic-law-integrated deep learning method for high-dimensional sensor fault detection in diverse complex HVAC systems," Applied Energy, Elsevier, vol. 351(C).
    4. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
    5. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
    6. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    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. Rongjiang Ma & Xianlin Wang & Ming Shan & Nanyang Yu & Shen Yang, 2020. "Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data," Energies, MDPI, vol. 13(18), pages 1-14, September.
    9. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
    10. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    11. Gertsvolf, David & Horvat, Miljana & Aslam, Danesh & Khademi, April & Berardi, Umberto, 2024. "A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic images," Applied Energy, Elsevier, vol. 360(C).
    12. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    13. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
    14. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    15. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
    16. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    17. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    18. William Nelson & Charles Culp, 2023. "FDD in Building Systems Based on Generalized Machine Learning Approaches," Energies, MDPI, vol. 16(4), pages 1-16, February.
    19. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang & Fang, Xi, 2021. "A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems," Applied Energy, Elsevier, vol. 282(PB).
    20. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5534-:d:876157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.