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Hybrid and co-learning approach for anomalies prediction and explanation of wind turbine systems

2024
journal article
article
10.1016/j.engappai.2024.108046
Journal
Engineering Applications of Artificial Intelligence
140
Author
Rajaoarisoa Lala

Kuk Michał

Bobek Szymon

Sayed-Mouchaweh Moamar
Volume
133
Article ID
108046
ISSN
0952-1976
eISSN
1873-6769
Keywords in English

co-learning

hybrid diagnoser

autoencoder model

discrete event systems

anomalies prediction

anomalies explanation

rule-based

network models

wind turbines

Language
English
Journal language
English
Affiliation
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej
Scopus© citations
7
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanejpl
dc.contributor.authorRajaoarisoa, Lalapl
dc.contributor.authorKuk, Michałpl
dc.contributor.authorBobek, Szymon - 428058 pl
dc.contributor.authorSayed-Mouchaweh, Moamarpl
dc.date.accessioned2024-02-22T17:08:44Z
dc.date.available2024-02-22T17:08:44Z
dc.date.issued2024pl
dc.description.volume133pl
dc.identifier.articleid108046pl
dc.identifier.doi10.1016/j.engappai.2024.108046pl
dc.identifier.eissn1873-6769pl
dc.identifier.issn0952-1976pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/327366
dc.languageengpl
dc.language.containerengpl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licenceBez licencji otwartego dostępu
dc.source.integratorfalse
dc.subject.enco-learningpl
dc.subject.enhybrid diagnoserpl
dc.subject.enautoencoder modelpl
dc.subject.endiscrete event systemspl
dc.subject.enanomalies predictionpl
dc.subject.enanomalies explanationpl
dc.subject.enrule-basedpl
dc.subject.ennetwork modelspl
dc.subject.enwind turbinespl
dc.subtypeArticlepl
dc.titleHybrid and co-learning approach for anomalies prediction and explanation of wind turbine systemspl
dc.title.journalEngineering Applications of Artificial Intelligencepl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej
dc.contributor.authorpl
Rajaoarisoa, Lala
dc.contributor.authorpl
Kuk, Michał
dc.contributor.authorpl
Bobek, Szymon - 428058
dc.contributor.authorpl
Sayed-Mouchaweh, Moamar
dc.date.accessioned
2024-02-22T17:08:44Z
dc.date.available
2024-02-22T17:08:44Z
dc.date.issuedpl
2024
dc.description.volumepl
133
dc.identifier.articleidpl
108046
dc.identifier.doipl
10.1016/j.engappai.2024.108046
dc.identifier.eissnpl
1873-6769
dc.identifier.issnpl
0952-1976
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/327366
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
Bez licencji otwartego dostępu
dc.source.integrator
false
dc.subject.enpl
co-learning
dc.subject.enpl
hybrid diagnoser
dc.subject.enpl
autoencoder model
dc.subject.enpl
discrete event systems
dc.subject.enpl
anomalies prediction
dc.subject.enpl
anomalies explanation
dc.subject.enpl
rule-based
dc.subject.enpl
network models
dc.subject.enpl
wind turbines
dc.subtypepl
Article
dc.titlepl
Hybrid and co-learning approach for anomalies prediction and explanation of wind turbine systems
dc.title.journalpl
Engineering Applications of Artificial Intelligence
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations
Wydział Fizyki, Astronomii i Informatyki Stosowanej
Bobek, Szymon
No affiliation
Rajaoarisoa, Lala
Kuk, Michał
Sayed-Mouchaweh, Moamar

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