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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Panayiotis M. Papadopoulos, Vasso Reppa, Marios M. Polycarpou and Christos G. Panayiotou, "Scalable Distributed Sensor Fault Diagnosis for Smart Buildings," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 638-655, May 2020. doi: 10.1109/JAS.2020.1003123
Citation: Panayiotis M. Papadopoulos, Vasso Reppa, Marios M. Polycarpou and Christos G. Panayiotou, "Scalable Distributed Sensor Fault Diagnosis for Smart Buildings," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 638-655, May 2020. doi: 10.1109/JAS.2020.1003123

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

doi: 10.1109/JAS.2020.1003123
Funds:  This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme (739551) (KIOS CoE)
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  • The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

     

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    Highlights

    • Fault diagnosis for energy systems.
    • Distributed fault diagnosis for smart buildings.
    • Formulation of complex HVAC building systems as a network of strongly interconnected subsystems.
    • Design of a distributed, model-based algorithm for sensor fault detection and isolation in large-scale HVAC systems.
    • Sensor fault diagnosis algorithm enhanced with robustness, improved detectability and scalability.

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