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

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

The 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) techn...

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Bibliographic Details
Main Authors: Sondes Gharsellaoui, Majdi Mansouri, Mohamed Trabelsi, Mohamed-Faouzi Harkat, Shady S. Refaat, Hassani Messaoud
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
HVAC systems
machine learning (ML)
model uncertainties
feature extraction and selection
interval-valued principal component analysis (IPCA)
fault detection and diagnosis (FDD)
Online Access:https://ieeexplore.ieee.org/document/9176993/

Internet

https://ieeexplore.ieee.org/document/9176993/

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