Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

Sondes Gharsellaoui, Majdi Mansouri, Shady S. Refaat, Haitham Abu-Rub and Hassani Messaoud
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Sondes Gharsellaoui: Electrical Engineering Department, Laboratory of Automatic Signal and Image Processing, National Higher Engineering School of Tunis, University of Tunis, Avenue Taha Hussein Montfleury, 1008 Tunis, Tunisia
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Education City, 77874 Doha, Qatar
Shady S. Refaat: Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Education City, 77874 Doha, Qatar
Haitham Abu-Rub: Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Education City, 77874 Doha, Qatar
Hassani Messaoud: Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, University of Monastir, 5019 Monastir, Tunisia

Energies, 2020, vol. 13, issue 3, 1-16

Abstract: Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.

Keywords: machine learning (ML); principal component analysis (PCA); air conditioning systems; feature extraction; fault detection; fault classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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