prikaz prve stranice dokumenta Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra
  Download
PDF 12.07 MB
master's thesis
Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra
2019. urn:nbn:hr:200:003578
Juroš, Tomislav
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Electromechanical Engineering
Chair of Fundamentals of Electrical Engineering and Measurements

Cite this document

Juroš, T. (2019). Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:003578

Juroš, Tomislav. "Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:003578

Juroš, Tomislav. "Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:003578

Juroš, T. (2019). 'Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:200:003578

Juroš T. Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2019 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:200:003578

T. Juroš, "Detekcija kvarnih stanja asinkronog motora analizom strujnog spektra", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:200:003578

Please login to the repository to save this object to your list.
Metadata
TitleDetekcija kvarnih stanja asinkronog motora analizom strujnog spektra
Title (english)Current spectrum analysis for induction machine fault detection
AuthorTomislav Juroš
MentorMarinko Barukčić (mentor)
MentorTin Benšić (mentor)
Committee memberMarinko Barukčić (predsjednik povjerenstva)
Committee memberŽeljko Hederić (član povjerenstva)
Committee memberVedrana Jerković Štil (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Electromechanical Engineering)
(Chair of Fundamentals of Electrical Engineering and Measurements)
Osijek
Defense date and country2019-04-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Electromechanical Engineering
Abstract
U ovom diplomskom radu je opisana jedna od najznacajnih on 􀀀 line metoda za di- jagnostiku kvara motora (MCSA). Ova metoda je bazirana na snimanju statorskih struja i spektralnoj analizi putem brze Fourierove transformacije. Fourierov spektar moze pomoci pri odredivanju karakteristicnih frekvencija kvarova. Promatrajuci ispravan motor u odnosu na kvarno stanje, moze se zakljuciti o kakvom kvaru se radi. Pri snimanju struja, motor treba biti opterecen teretom od barem 70% nazivnog momenta. Na temelju rezultata, kreiran je algoritam koji je baziran na omjerima energije signala i koji odlucuje o tipu kvara.
Abstract (english)
One of the most signicant on line diagnostic methods for fault condition of induction motor (MCSA) has been described in this master thesis. This method is based on recording stator currents and spectral analysis through fast Fourier transformation. The Fourier spectrum can help to determine the characteristic fault frequencies. Considering a healthy motor in relation to unhealthy motor, it can be concluded about type of failure state. When recording the current, the motor must be loaded with a load of at least 70 % of the nominal torque. Based on the results, an algorithm, which is based on the signal energy ratios and decides about type of failure, was created.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:003578
Study programmeTitle: Graduate University Study Programme in Electrical Engineering; specializations in: Communications and Informatics, Power Engineering
Course: Communications and Informatics
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 In copyright icon
Created on2019-04-30 13:14:37