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

Simple item page
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 10.1109/ACCESS.2020.3019365
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.subjectHVAC systems
dc.subjectmachine learning (ML)
dc.subjectmodel uncertainties
dc.subjectfeature extraction and selection
dc.subjectinterval-valued principal component analysis (IPCA)
dc.subjectfault detection and diagnosis (FDD)
dc.titleInterval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
HARKAT Mohamed faouzi - Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems.pdf
Size:
1.06 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Articles