IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v327y2022ics0306261922013071.html
   My bibliography  Save this article

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Author

Listed:
  • Chen, Yimin
  • Wen, Jin
  • Pradhan, Ojas
  • Lo, L. James
  • Wu, Teresa

Abstract

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

Suggested Citation

  • Chen, Yimin & Wen, Jin & Pradhan, Ojas & Lo, L. James & Wu, Teresa, 2022. "Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013071
    DOI: 10.1016/j.apenergy.2022.120050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922013071
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    2. 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.
    3. 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.
    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. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    2. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    3. Fan, Cheng & Wu, Qiuting & Zhao, Yang & Mo, Like, 2024. "Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance," Applied Energy, Elsevier, vol. 356(C).

    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. 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.
    2. 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).
    3. 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).
    4. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.
    5. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    6. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
    7. Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).
    8. Monadi, Mehdi & Zamani, M. Amin & Koch-Ciobotaru, Cosmin & Candela, Jose Ignacio & Rodriguez, Pedro, 2016. "A communication-assisted protection scheme for direct-current distribution networks," Energy, Elsevier, vol. 109(C), pages 578-591.
    9. ValinÄ ius, Mindaugas & ŽutautaitÄ—, Inga & Dundulis, Gintautas & RimkeviÄ ius, Sigitas & Janulionis, Remigijus & Bakas, Rimantas, 2015. "Integrated assessment of failure probability of the district heating network," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 314-322.
    10. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    11. 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).
    12. 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).
    13. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
    14. Sun, Chunhua & Zhang, Haixiang & Cao, Shanshan & Xia, Guoqiang & Zhong, Jian & Wu, Xiangdong, 2023. "A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system," Applied Energy, Elsevier, vol. 349(C).
    15. V.K. Arun Shankar & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg & S. Paramasivam, 2019. "Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System," Energies, MDPI, vol. 12(7), pages 1-14, April.
    16. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    17. Borge-Diez, David & Colmenar-Santos, Antonio & Pérez-Molina, Clara & López-Rey, África, 2015. "Geothermal source heat pumps under energy services companies finance scheme to increase energy efficiency and production in stockbreeding facilities," Energy, Elsevier, vol. 88(C), pages 821-836.
    18. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.
    19. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    20. 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.

    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:eee:appene:v:327:y:2022:i:c:s0306261922013071. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.