Support vector machine based novelty detection and FDD framework applied to building AHU systems
Visualitza/Obre
10.1109/ETFA46521.2020.9212088
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/339885
Tipus de documentText en actes de congrés
Data publicació2020
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
The increasing energy consumption of heating, ventilation and air conditioning (HVAC) systems is one of the main concerns in the building sector. Fault detection technologies are now indispensable for energy efficiency and performance improvement. In this paper, a methodology for the robust and reliable fault detection and diagnosis is presented as a two-stage framework composed by an offline stage where the models are built and an online stage that is constantly receiving new samples. The system includes a novelty detection scheme developed using one-class support vector machines (OC-SVM) and a classifier built using SVM. The proposed strategy is applied to a dataset for a single-zone constant air volume air handling unit. The experimental results show that the novelty detection stage adds robustness layer to the typical classification scheme.
Descripció
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CitacióMartinez, V. [et al.]. Support vector machine based novelty detection and FDD framework applied to building AHU systems. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1749-1754. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212088.
ISBN978-1-7281-8957-4
Versió de l'editorhttps://ieeexplore.ieee.org/document/9212088
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