IEEE Access | |
Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems | |
Sondes Gharsellaoui1  Mohamed Trabelsi1  Shady S. Refaat2  Hassani Messaoud3  Majdi Mansouri4  Mohamed-Faouzi Harkat4  | |
[1] Electrical and Computer Engineering Program, Texas A&x0026;Electronic and Communications Engineering Department, Kuwait College of Science and Technology, Safat, Kuwait;LASMA, Badji Mokhtar - Annaba University, Annaba, Algeria;M University at Qatar, Doha, Qatar; | |
关键词: HVAC systems; machine learning (ML); model uncertainties; feature extraction and selection; interval-valued principal component analysis (IPCA); fault detection and diagnosis (FDD); | |
DOI : 10.1109/ACCESS.2020.3019365 | |
来源: DOAJ |
【 摘 要 】
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) 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 confirm the high-efficiency 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.
【 授权许可】
Unknown