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

Hanen Chaouch, Samia Charfeddine, Sondess Ben Aoun, Houssem Jerbi and Víctor Leiva
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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

Mathematics, 2022, vol. 10, issue 6, 1-16

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.

Keywords: artificial intelligence; discrete wavelet transform; fault detection and isolation; kernel method; principal component analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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