Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors
Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decom...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | México |
| Institución: | Universidad de Guanajuato |
| Repositorio: | Redalyc-UG |
| OAI Identifier: | oai:redalyc.org:47436895015 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=47436895015 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería high multiple fault diagnosis induction motors Empirical mode decomposition |
| Sumario: | Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faul ts which provides an accurate and effective strategy for the motor condition diagnosis. |
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