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

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

doi:10.3390/math10060890

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.

Highlights

Introduction and Bibliographical Review iationsMachine learning and artificial intelligence algorithms, as well as big data tools, have taken a prominent place in the determination of strategies and management of companies that have understood their relevance [1–8]; a study on machine learning methods for automatic defects detection was conducted in [1]
Machine learning and statistical learning refer to a set of learning algorithms, supervised or not, and/or by reinforcement, obtained from historical data, allowing the solution of a problem
Machine learning techniques are numerous in the literature [27,28], but we focus on fault detection and isolation (FDI) methods, which combine statistical analysis and wavelet networks

Summary

Introduction

Introduction and Bibliographical Review iationsMachine learning and artificial intelligence algorithms, as well as big data tools, have taken a prominent place in the determination of strategies and management of companies that have understood their relevance [1–8]; a study on machine learning methods for automatic defects detection was conducted in [1]. Some manufacturers have seized the opportunity offered by these predictive algorithms to establish their leadership in the market. Machine learning and statistical learning refer to a set of learning algorithms, supervised or not, and/or by reinforcement, obtained from historical data, allowing the solution of a problem. These algorithms permit us to interpret data without explicit or deterministic programming and to manage a strategy making operational decisions. Modern production systems are gradually incorporating machine learning predictive algorithms into all stages of the manufacturing process

Methods

Results

Conclusion