| Título |
Oil Whirl Fault Detection in Induction Motors using Orbital Analysis and Neural Networks |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
2016 SAI Intelligent Systems Conference (INTELLISYS) |
| Resumen |
Fault detection in induction motors is a useful practice when some critical processes depend on good machines performance. This work proposes a new computational model for detecting oil whirl faults in induction motors using orbital patterns. Signal vibrations are measured and pre-processed in order to obtain a characteristic orbit that represents the motor condition where an oil whirl fault is present. Through an artificial neural network, the orbital patterns are classified according to the motor condition: good or faulty. Experimental results show a good performance for the proposed model, providing a new tool for recognizing problems in induction motors. |
| Observaciones |
DOI 10.1007/978-3-319-56994-9_20
Lecture Notes in Networks and Systems, v. 15 |
| Lugar |
Londres |
| País |
Inglaterra |
| No. de páginas |
286-296 |
| Vol. / Cap. |
|
| Inicio |
2016-09-21 |
| Fin |
2016-09-22 |
| ISBN/ISSN |
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