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Title:Spektralna razčlenitev tresljajev poškodovanega ležaja s PSoC 6 in UI : diplomsko delo
Authors:ID Brglez, Jakob (Author)
ID Uran, Suzana (Mentor) More about this mentor... New window
ID Karner, Timi (Mentor) More about this mentor... New window
ID Bratina, Božidar (Comentor)
Files:.pdf UN_Brglez_Jakob_2026.pdf (3,81 MB)
MD5: A7594F6EB639B8E0D08B7D2E2DD06B26
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Naloga obsega teoretični pregled poškodb ležajev in metod za njihovo zaznavo ter praktično izvedbo meritve tresljajev ležajev rabljenega in novega ventilatorja ter presojo prisotnosti poškodb ležajev z metodami z in brez umetne inteligence. Meritve in osnovna obdelava podatkov so bile izvedene z razvojno ploščico PSoC 6 AI Evaluation Board, pri čemer je bil napisan program za tipanje tresljajev z višjo frekvenco tipanja. Za razčlenitev signalov tresljajev sta bili uporabljeni metodi s Fourierjevo transformacijo in razčlenitvijo spektra ovojnice. Zbrani podatki so ob podatkih iz javne zbirke služili tudi za učenje konvolucijske nevronske mreže za klasifikacijo signalov med poškodovane in nepoškodovane.
Keywords:poškodbe ležajev, tresljaji, razčlenitev signalov, umetna inteligenca, nevronske mreže
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Brglez]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (IX, 55 str.))
PID:20.500.12556/DKUM-98748 New window
UDC:621.7.08:621.822(043.2)
COBISS.SI-ID:290197507 New window
Publication date in DKUM:18.08.2026
Views:342
Downloads:32
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:05.07.2026

Secondary language

Language:English
Title:Spectral analysis of faulty bearing vibrations using PSoC 6 and AI
Abstract:The work encompasses a theoretical review of bearing faults and their detection methods as well as practical execution of bearing vibration measurement of an used and an unused fan with an assessment of presence of faults with or without the use of artificial intelligence. The measurements and basic processing of data is executed with PSoC 6 AI Evaluation Board, where a program was developed for sampling the vibration with a higher sampling frequency. Fourier transform and envelope spectrum analysis methods were used for vibration analysis. The acquired data with a portion of data from a public dataset was used for training a convolutional neural network, meant for classification of signals into those with and those without faults.
Keywords:bearing faults, vibration, signal analysis, artificial intelligence, neural networks


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