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Article Dans Une Revue Journal of Building Performance Simulation Année : 2022

The use of dimensionality reduction techniques for fault detection and diagnosis in a AHU unit: critical assessment of its reliability

Résumé

Fault Detection and Diagnosis (FDD) are important tools to perform on-going monitoring of the systems and help in their building commissioning. An innovative method is investigated based on combined data-driven and knowledge-based approaches. This article presents the method. First phase, a so-called operating map of the system is built using dimension reduction method and numerical or experimental dataset. This map is composed of several regions corresponding to nominal operation and to specific faults. The second phase focuses on the FDD. The monitored data are projected on the map. According to the position, a clear and precise fault detection and diagnosis can be carried. The method is applied to an air handling unit. The map is built using data generated with a building simulation program. The reliability of the method is proven using experimental data of nominal and fault operation generated.
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Dates et versions

hal-03833250 , version 1 (08-11-2022)

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Citer

Hugo Geoffroy, Julien Berger, Benoît Colange, Sylvain Lespinats, Denys Dutykh. The use of dimensionality reduction techniques for fault detection and diagnosis in a AHU unit: critical assessment of its reliability. Journal of Building Performance Simulation, 2022, ⟨10.1080/19401493.2022.2080864⟩. ⟨hal-03833250⟩
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