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

<p>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 (...

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Bibliographic Details
Main Author: Sondes Gharsellaoui (16870047) (author)
Other Authors: Majdi Mansouri (16869885) (author), Mohamed Trabelsi (16869891) (author), Mohamed-Faouzi Harkat (16869897) (author), Shady S. Refaat (16864269) (author), Hassani Messaoud (16870050) (author)
Published: 2020
Subjects:
Engineering
Electrical engineering
Mechanical engineering
Information and computing sciences
Machine learning
Buildings
Fault detection and diagnosis (FDD)
Feature extraction
Feature extraction and selection
HVAC systems
Interval-valued principal component analysis (IPCA)
Machine learning (ML)
Measurement errors
Monitoring
Model uncertainties
Principal component analysis
Standardization
Uncertainty
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