Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Saberi, Alireza Nemat | |
| dc.contributor.author | Sandirasegaram, Sarvavignoban | |
| dc.contributor.author | Belahcen, Anouar | |
| dc.contributor.author | Vaimann, Toomas | |
| dc.contributor.author | Šobra, Jan | |
| dc.date.accessioned | 2021-01-25T11:00:26Z | - |
| dc.date.available | 2021-01-25T11:00:26Z | - |
| dc.date.issued | 2020 | |
| dc.identifier.citation | SABERI, A.N., SANDIRASEGARAM, S., BELAHCEN, A., VAIMANN, T., ŠOBRA, J. Multi-sensor fault diagnosis of induction motors using random forests and support vector machine. In: Proceedings : 2020 International Conference on Electrical Machines (ICEM 2020). Piscataway: IEEE, 2020. s. 1404-1410. ISBN 978-1-72819-945-0. | cs |
| dc.identifier.isbn | 978-1-72819-945-0 | |
| dc.identifier.uri | 2-s2.0-85098620675 | |
| dc.identifier.uri | http://hdl.handle.net/11025/42546 | |
| dc.format | 7 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | Proceedings : 2020 International Conference on Electrical Machines (ICEM 2020) | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © IEEE | en |
| dc.title | Multi-sensor fault diagnosis of induction motors using random forests and support vector machine | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | 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 | en |
| dc.subject.translated | fault diagnosis | en |
| dc.subject.translated | induction motor | en |
| dc.subject.translated | machine learning | en |
| dc.subject.translated | multiple signal classification | en |
| dc.subject.translated | support vector machine | en |
| dc.identifier.doi | 10.1109/ICEM49940.2020.9270689 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43931209 | |
| Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference Papers (KEV) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| ICEM2020_Sobra.pdf | 870,48 kB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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