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Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

Author

Listed:
  • Hanen Chaouch

    (High Institute of Applied Sciences and Technology of Kairouan, University of Kairouan, Kairouan 3100, Tunisia)

  • Samia Charfeddine

    (Research Unit of Photovoltaic, Wind and Geothermal Systems, National Engineering School of Gabès, University of Gabès, Gabès 6011, Tunisia)

  • Sondess Ben Aoun

    (Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 1234, Saudi Arabia)

  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Ha’il, Ha’il 1234, Saudi Arabia)

  • Víctor Leiva

    (School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

Abstract

In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform. The principle of our proposal involved decomposing multivariate data into wavelet coefficients by employing the discrete wavelet transform. Then, the kernel PCA was applied on every matrix of coefficients to detect defects. Only those scales that manifest overruns of the squared prediction errors in control limits were considered in the data reconstruction phase. Thus, the kernel PCA was approached on the reconstructed matrix for detecting defects and isolation. This approach exploits the kernel PCA performance for nonlinear process monitoring in combination with multiscale analysis when processing time-frequency scales. The proposed method was validated on a photovoltaic system related to a complex industrial process. A data matrix was determined from the variables that characterize this process corresponding to motor current, angular speed, convertor output voltage, and power voltage system output. We tested the developed methodology on 1000 observations of photovoltaic variables. A comparison with monitoring methods based on neural PCA was established, proving the efficiency of the developed methodology.

Suggested Citation

  • Hanen Chaouch & Samia Charfeddine & Sondess Ben Aoun & Houssem Jerbi & Víctor Leiva, 2022. "Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:890-:d:768499
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    References listed on IDEAS

    as
    1. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    2. Myong K. Jeong & Di Chen & Jye‐Chyi Lu, 2003. "Thresholded scalogram and its applications in process fault detection," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(3), pages 231-244, July.
    3. Carlos Martin-Barreiro & John A. Ramirez-Figueroa & Ana B. Nieto-Librero & Víctor Leiva & Ana Martin-Casado & M. Purificación Galindo-Villardón, 2021. "A New Algorithm for Computing Disjoint Orthogonal Components in the Three-Way Tucker Model," Mathematics, MDPI, vol. 9(3), pages 1-22, January.
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    Citations

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    Cited by:

    1. Adel Mellit & Chadia Zayane & Sahbi Boubaker & Souad Kamel, 2023. "A Sustainable Fault Diagnosis Approach for Photovoltaic Systems Based on Stacking-Based Ensemble Learning Methods," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Obaid Alshammari & Mourad Kchaou & Houssem Jerbi & Sondess Ben Aoun & Víctor Leiva, 2022. "A Fuzzy Design for a Sliding Mode Observer-Based Control Scheme of Takagi-Sugeno Markov Jump Systems under Imperfect Premise Matching with Bio-Economic and Industrial Applications," Mathematics, MDPI, vol. 10(18), pages 1-28, September.
    3. Houssem Jerbi & Izzat Al-Darraji & Georgios Tsaramirsis & Lotfi Ladhar & Mohamed Omri, 2023. "Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System," Mathematics, MDPI, vol. 11(6), pages 1-32, March.
    4. Samia Charfeddine & Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Víctor Leiva, 2022. "A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems," Mathematics, MDPI, vol. 10(12), pages 1-26, June.

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