IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i3p609-d314946.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/3/609/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/3/609/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Carlos Moreno-Miranda & Hipatia Palacios & Daniele Rama, 2019. "Small-holders perception of sustainability and chain coordination: evidence from Arriba PDO Cocoa in Western Ecuador," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 8(3), December.
    3. Fernandez-Haddad, Zaira & Quiroga, Sonia, 2011. "Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114358, European Association of Agricultural Economists.
    4. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    5. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    6. Heni Masruroh & Soemarno Soemarno & Syahrul Kurniawan & Amin Setyo Leksono, 2023. "A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    7. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    8. Hugh L. Christensen, 2015. "Algorithmic arbitrage of open-end funds using variational Bayes," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-38, December.
    9. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    10. Mirza Pasic & Halima Hadziahmetovic & Ismira Ahmovic & Mugdim Pasic, 2023. "Principal Component Regression Modeling and Analysis of PM 10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    11. Cai, Yuezhou & Hanley, Aoife, 2012. "Building BRICS: 2-Stage DEA analysis of R&D efficiency," Kiel Working Papers 1788, Kiel Institute for the World Economy (IfW Kiel).
    12. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    13. Elkin Castaño & Santiago Gallón, 2017. "A solution for multicollinearity in stochastic frontier production function models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 9-23, Enero - J.
    14. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    15. Ranjith Vijayakumar & Ji Yeh Choi & Eun Hwa Jung, 2022. "A Unified Neural Network Framework for Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1503-1528, December.
    16. Mishra, Aditya & Dey, Dipak K. & Chen, Yong & Chen, Kun, 2021. "Generalized co-sparse factor regression," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    17. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Aug 2024.
    18. Santiago Velásquez & Juho Kanniainen & Saku Mäkinen & Jaakko Valli, 2018. "Layoff announcements and intra-day market reactions," Review of Managerial Science, Springer, vol. 12(1), pages 203-228, January.
    19. Arpad Gellert & Stefan-Alexandru Precup & Alexandru Matei & Bogdan-Constantin Pirvu & Constantin-Bala Zamfirescu, 2022. "Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions," Mathematics, MDPI, vol. 10(15), pages 1-21, August.
    20. Arjan J. Frederiks & Sílvia Costa & Boudewijn Hulst & Aard J. Groen, 2024. "The early bird catches the worm: The role of regulatory uncertainty in early adoption of blockchain’s cryptocurrency by fintech ventures," Journal of Small Business Management, Taylor & Francis Journals, vol. 62(2), pages 790-823, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:609-:d:314946. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.