Detección de fallos en imanes rotos en pmsm mediante una red neuronal convolucional y SVM
DOI:
https://doi.org/10.5935/jetia.v10i48.1185Resumen
El motor síncrono de imanes permanentes (PMSM) es un componente fundamental en diversas aplicaciones.
aplicaciones, pero sigue siendo susceptible a una serie de fallos tanto en el rotor como en el estator.
estator, surge la necesidad imperiosa de abordar estos problemas de forma rápida e inteligente. En este
estudio, se adoptó un enfoque novedoso en el que se conceptualizó un diseño de PMSM en el programa
el programa Ansys Maxwell, seguido de la introducción deliberada de un fallo en el nivel magnético del rotor.
nivel magnético del rotor. Específicamente, se delinearon tres escenarios de falla distintos basados en el
número de imanes rotos (BM), a saber, 2, 3 y 4, localizados en zonas específicas del rotor.
En particular, se seleccionó la densidad de flujo magnético como parámetro central de esta investigación.
Para detectar y diagnosticar eficazmente los fallos derivados de los BM, se utilizó una innovadora arquitectura de red neuronal convolucional (CNN).
Convolucional (CNN). Aprovechando imágenes del diseño del PMSM
durante las fases operativas en varios intervalos de tiempo, la CNN mostró una notable eficacia a la hora de discernir y diagnosticar fallos derivados del BM.
la CNN mostró una notable eficacia a la hora de discernir y clasificar los casos de fallo. Tras analizar los resultados
Tras el análisis de los resultados obtenidos, se hace evidente que la CNN exhibió una precisión sin precedentes en la detección de fallos, alcanzando una notable tasa de éxito del 100%.
en la detección de fallos, alcanzando una notable tasa de éxito del 100% cuando se yuxtapone con
alternativas como las máquinas de vectores soporte (SVM) y los vecinos más cercanos (KNN),
que arrojaron índices de precisión del 97%.
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