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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sondes Gharsellaoui & Majdi Mansouri & Shady S. Refaat & Haitham Abu-Rub & Hassani Messaoud, 2020. "Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches," Energies, MDPI, vol. 13(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:609-:d:314946
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    References listed on IDEAS

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    1. Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    2. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    3. Max Emil S. Trothe & Hamid Reza Shaker & Muhyiddine Jradi & Krzysztof Arendt, 2019. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings," Energies, MDPI, vol. 12(9), pages 1-12, April.
    4. Mahendra Singh & Nguyen Trung Kien & Houda Najeh & Stéphane Ploix & Antoine Caucheteux, 2019. "Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test," Energies, MDPI, vol. 12(13), pages 1-22, June.
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