Publication: Fault Identification in Induction Motors with RBF Neural Network Based on Dynamical PCA
| dc.authorscopusid | 22953804000 | |
| dc.authorscopusid | 22433630600 | |
| dc.authorscopusid | 22433319300 | |
| dc.contributor.author | Kilic, E. | |
| dc.contributor.author | Özgönenel, O. | |
| dc.contributor.author | Özdemir, A.E. | |
| dc.date.accessioned | 2020-06-21T15:24:22Z | |
| dc.date.available | 2020-06-21T15:24:22Z | |
| dc.date.issued | 2007 | |
| dc.department | Ondokuz Mayıs Üniversitesi | en_US |
| dc.department-temp | [Kilic] Erdal, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Özgönenel] Okan, Electrical and Electronics Engineering Department, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Özdemir] Ali Ekber, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey | en_US |
| dc.description | IEEE Industry Application Society; IEEE Power Electronics Society; IEEE Power Engineering Society; IEEE Industrial Electronics Society | en_US |
| dc.description.abstract | Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved efficiency of induction motors running off power supply mains. In the applications of three-phase induction motors in industry, the inner faults may occur in their rotor and stator windings. These kinds of faults will make serious health problems on the motor. This paper presents a new protection scheme for internal short circuit faults occurring with a degree (single or multiple) in three-phase induction motors. The results are compared with traditional outcomes existed from Fast Fourier Transformation (FFT) of the motor currents. The proposed algorithm is simpler and only uses stator currents. There is no need any other sensor knowledge. © 2007 IEEE. | en_US |
| dc.identifier.doi | 10.1109/IEMDC.2007.382776 | |
| dc.identifier.endpage | 835 | en_US |
| dc.identifier.isbn | 9781424407439 | |
| dc.identifier.isbn | 1424407435 | |
| dc.identifier.scopus | 2-s2.0-35048855523 | |
| dc.identifier.startpage | 830 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/IEMDC.2007.382776 | |
| dc.identifier.volume | 1 | en_US |
| dc.identifier.wos | WOS:000248118800145 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | -- IEEE International Electric Machines and Drives Conference, IEMDC 2007 | en_US |
| dc.relation.journal | Ieee Iemdc 2007: Proceedings of the International Electric Machines and Drives Conference, Vols 1 and 2 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Fault Identification (FI) | en_US |
| dc.subject | FFT | en_US |
| dc.subject | Induction Motor | en_US |
| dc.subject | Internal Faults | en_US |
| dc.subject | Principal Component Analysis (PCA) | en_US |
| dc.subject | Radial Basis Functions (RBFs) | en_US |
| dc.title | Fault Identification in Induction Motors with RBF Neural Network Based on Dynamical PCA | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication |