Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

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dc.contributor.authorHARKAT Mohamed Faouzi (Co-Auteur)
dc.date.accessioned2025-09-16T11:12:45Z
dc.date.available2025-09-16T11:12:45Z
dc.date.issued2020-08
dc.descriptionVOLUME 8, 2020 Digital Object Identifier 10.1109
dc.description.abstractThe operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results con rm the high-ef ciency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.
dc.identifier.urihttp://dspace.ensti-annaba.dz:4000/handle/123456789/835
dc.language.isoen
dc.publisherIEEE Access
dc.titleInterval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems
dc.typeArticle
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