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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Mathematics

سال: 2022

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math10060890