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master's thesis
Dijagnostika elektromotora modelima umjetne inteligencije
2022. urn:nbn:hr:190:557748
Ladić, Tin
University of Rijeka
Faculty of Engineering
Department of Automation and Electronics
Section of Electronics, Robotics and Automation

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Ladić, T. (2022). Dijagnostika elektromotora modelima umjetne inteligencije (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:557748

Ladić, Tin. "Dijagnostika elektromotora modelima umjetne inteligencije." Master's thesis, University of Rijeka, Faculty of Engineering, 2022. https://urn.nsk.hr/urn:nbn:hr:190:557748

Ladić, Tin. "Dijagnostika elektromotora modelima umjetne inteligencije." Master's thesis, University of Rijeka, Faculty of Engineering, 2022. https://urn.nsk.hr/urn:nbn:hr:190:557748

Ladić, T. (2022). 'Dijagnostika elektromotora modelima umjetne inteligencije', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 08 January 2025, https://urn.nsk.hr/urn:nbn:hr:190:557748

Ladić T. Dijagnostika elektromotora modelima umjetne inteligencije [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2022 [cited 2025 January 08] Available at: https://urn.nsk.hr/urn:nbn:hr:190:557748

T. Ladić, "Dijagnostika elektromotora modelima umjetne inteligencije", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:190:557748

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Metadata
TitleDijagnostika elektromotora modelima umjetne inteligencije
Title (english)Electromotor diagnostics using artificial intelligence methods
AuthorTin Ladić
MentorZlatan Car (mentor)
Committee memberSaša Vlahinić (predsjednik povjerenstva)
Committee memberZlatan Car (član povjerenstva)
Committee memberIvan Volarić (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Automation and Electronics)
(Section of Electronics, Robotics and Automation)
Rijeka
Defense date and country2022-09-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Basic Technical Sciences
Automatics
Abstract
U ovome diplomskom radu obrađene su metode dijagnostike elektromotornih pogona korištenjem umjetne inteligencije. Predstavljeni su i opisani algoritmi XGBoost i MLPClassifier koji su se primjenjivali za dijagnostiku u ovom radu, te je opisana analiza glavnih komponenti (PCA) i metoda uklanjanja jako koreliranih podataka kao metode koje se koriste za predobradu podataka. Uspoređeni su rezultati korištenjem oba algoritma bez predobrade i sa predobradom podataka, te je XGBoost algoritam s uklonjenim jako koreliranim podatcima odabran kao najbolji alat za dijagnostiku elektromotornih pogona metodama umjetne inteligencije u ovome slučaju.
Abstract (english)
This master thesis describes methods of electric drive fault diagnosis using artificial intelligence. XGBoost and MLPClassifier algorithms are described and used in this thesis, alongside Primary Component Analysis (PCA) and removal of highly correlated data as data preprocessing methods. Described algorithms are tested using original data and preprocessed data, the results are compared. XGBoost classification using preprocessed data via the removal of highly correlated data is deemed the best for fault diagnosis in this case.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:557748
Study programmeTitle: Graduate University Study of Electrical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2022-09-20 14:56:42