IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1601-d226351.html
   My bibliography  Save this article

Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings

Author

Listed:
  • Max Emil S. Trothe

    (Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

  • Hamid Reza Shaker

    (Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

  • Muhyiddine Jradi

    (Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

  • Krzysztof Arendt

    (Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To the best of our knowledge, this is the first time that this method is used for applications in smart buildings. First a dynamic model for a zone in a real-case building is developed in which faults are also introduced. Then fault diagnosability is investigated by analyzing the fault isolability of the model. Based on the investigation, it was concluded that not all the faults in the model are diagnosable. Then an approach for placing new sensors is implemented. It is observed that for two test scenarios, placing additional sensors in the model leads to full diagnosability. Since sensors placement is key for an effective FDD process, the optimal placement of such sensors is also studied in this work. A case study of campus building OU44 at the University of Southern Denmark is considered. The results show that as the system gets more complicated by introducing more faults, additional sensors should be added to achieve full diagnosability.

Suggested Citation

  • Max Emil S. Trothe & Hamid Reza Shaker & Muhyiddine Jradi & Krzysztof Arendt, 2019. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings," Energies, MDPI, vol. 12(9), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1601-:d:226351
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1601/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1601/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    2. Polverino, Pierpaolo & Sorrentino, Marco & Pianese, Cesare, 2017. "A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems," Applied Energy, Elsevier, vol. 204(C), pages 1198-1214.
    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. 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.
    2. Sondes Gharsellaoui & Majdi Mansouri & Shady S. Refaat & Haitham Abu-Rub & Hassani Messaoud, 2020. "Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches," Energies, MDPI, vol. 13(3), pages 1-16, January.

    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. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    2. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
    3. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
    4. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    5. Younghoon Kwak & Jeonga Kang & Sun-Hye Mun & Young-Sun Jeong & Jung-Ho Huh, 2020. "Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis," Energies, MDPI, vol. 13(21), pages 1-22, November.
    6. Dayoung Jung & Youngtae Choe & Jihun Shin & Eunche Kim & Gihong Min & Dongjun Kim & Mansu Cho & Chaekwan Lee & Kilyong Choi & Byung Lyul Woo & Wonho Yang, 2022. "Risk Assessment of Indoor Air Quality and Its Association with Subjective Symptoms among Office Workers in Korea," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    7. Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
    8. Gallo, Marco & Costabile, Carmine & Sorrentino, Marco & Polverino, Pierpaolo & Pianese, Cesare, 2020. "Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems," Applied Energy, Elsevier, vol. 279(C).
    9. Yoon, Y. & Jung, S. & Im, P. & Salonvaara, M. & Bhandari, M. & Kunwar, N., 2023. "Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    10. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    11. Daly, Daniel & Carr, Chantel & Daly, Matthew & McGuirk, Pauline & Stanes, Elyse & Santala, Inka, 2023. "Extending urban energy transitions to the mid-tier: Insights into energy efficiency from the management of HVAC maintenance in ‘mid-tier’ office buildings," Energy Policy, Elsevier, vol. 174(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. Sorrentino, Marco & Bruno, Marco & Trifirò, Alena & Rizzo, Gianfranco, 2019. "An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications," Applied Energy, Elsevier, vol. 242(C), pages 1539-1548.
    14. Georgios Martinopoulos & Anna Serasidou & Panagiota Antoniadou & Agis M. Papadopoulos, 2018. "Building Integrated Shading and Building Applied Photovoltaic System Assessment in the Energy Performance and Thermal Comfort of Office Buildings," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    15. van Biert, L. & Visser, K. & Aravind, P.V., 2020. "A comparison of steam reforming concepts in solid oxide fuel cell systems," Applied Energy, Elsevier, vol. 264(C).
    16. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
    17. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
    18. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    19. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    20. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.

    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:12:y:2019:i:9:p:1601-:d:226351. 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.