Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
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e-ISSN
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Number of pages
2
Pages from-to
201-202
Publisher name
IEEE
Place of publication
Piscataway
Event location
Santa Clara,
Event date
Jun 5, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001046447800085