Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254747" target="_blank" >RIV/61989100:27240/23:10254747 - isvavai.cz</a>
Výsledek na webu
<a href="https://ieeexplore.ieee.org/document/10195129" target="_blank" >https://ieeexplore.ieee.org/document/10195129</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CAI54212.2023.00095" target="_blank" >10.1109/CAI54212.2023.00095</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Popis výsledku v původním jazyce
The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry's manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.
Název v anglickém jazyce
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Popis výsledku anglicky
The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry's manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
IEEE CAI 2023 : proceedings of the 2023 IEEE Conference on Artificial Intelligence : 5–6 June 2023, Santa Clara, California, USA
ISBN
979-8-3503-3985-7
ISSN
—
e-ISSN
—
Počet stran výsledku
2
Strana od-do
201-202
Název nakladatele
IEEE
Místo vydání
Piscataway
Místo konání akce
Santa Clara,
Datum konání akce
5. 6. 2023
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001046447800085