Multi-sensor fault diagnosis of induction motors using random forests and support vector machine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F20%3A43960210" target="_blank" >RIV/49777513:23220/20:43960210 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9270689" target="_blank" >https://ieeexplore.ieee.org/document/9270689</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICEM49940.2020.9270689" target="_blank" >10.1109/ICEM49940.2020.9270689</a>
Alternative languages
Result language
angličtina
Original language name
Multi-sensor fault diagnosis of induction motors using random forests and support vector machine
Original language description
This paper presents a fault diagnosis scheme for induction machines (IMs) using Support Vector Machine (SVM) and Random Forests (RFs). First, a number of timedomain and frequency-domain features are extracted from vibration and current signals in different operating conditions of IM. Then, these features are combined and considered as the input of SVM-based classification model. To avoid overfitting, RF is utilized to determine the most dominant features contributing to accurate classification. It is proved that the proposed method is capable of achieving highly accurate fault diagnosis results for broken rotor bar and eccentricity faults and it can appro
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings : 2020 International Conference on Electrical Machines (ICEM 2020)
ISBN
978-1-72819-945-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1404-1410
Publisher name
IEEE
Place of publication
Piscataway
Event location
virtual, Gothenburg, Sweden
Event date
Aug 23, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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