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Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test

Author

Listed:
  • Mahendra Singh

    (Laboratoire G-SCOP, UMR 5272, 46 Avenue Félix Viallet, 38031 Grenoble, France)

  • Nguyen Trung Kien

    (Laboratoire G-SCOP, UMR 5272, 46 Avenue Félix Viallet, 38031 Grenoble, France)

  • Houda Najeh

    (Laboratoire G-SCOP, UMR 5272, 46 Avenue Félix Viallet, 38031 Grenoble, France)

  • Stéphane Ploix

    (Laboratoire G-SCOP, UMR 5272, 46 Avenue Félix Viallet, 38031 Grenoble, France)

  • Antoine Caucheteux

    (Cerema Ouest, 23 Avenue de l’Amiral Chauvin, BP 20069-49136 Les Ponts-de- Cé cedex, Angers, France)

Abstract

Fault diagnosis and maintenance of a whole-building system is a complex task to perform. A wide range of available building fault detection and diagnosis (FDD) tools are only capable of performing fault detection using behavioral constraints analysis. However, the validity of the detected symptom is always questionable. In this work, we introduce the concept of the contextual test with validity constraints, in the context of building fault diagnostics. Thanks to a common formalization of the proposed heterogeneous tests, rule-, range-, and model-based tests can be combined in the same diagnostic analysis that reduces the whole-building modeling effort. The proposed methodology comprises the minimum diagnostic explanation feature that can significantly improve the knowledge of the building facility manager. A bridge diagnosis approach is used to describe the multiple fault scenarios. The proposed methodology is validated on an experimental building called the center for studies and design of prototypes (CECP) building located in Angers, France.

Suggested Citation

  • Mahendra Singh & Nguyen Trung Kien & Houda Najeh & Stéphane Ploix & Antoine Caucheteux, 2019. "Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test," Energies, MDPI, vol. 12(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2510-:d:244169
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    References listed on IDEAS

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    1. 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.
    2. Tae-Keun Oh & Donghwan Lee & Minsoo Park & Gichun Cha & Seunghee Park, 2018. "Three-Dimensional Visualization Solution to Building-Energy Diagnosis for Energy Feedback," Energies, MDPI, vol. 11(7), pages 1-18, July.
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    Cited by:

    1. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    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.

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