Feature extraction analysis using filter banks for faults classification in induction motors

Jhonattan Bulla, Alvaro D. Orjuela-Canon, Oscar D. Florez

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

9 Citas (Scopus)

Resumen

Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.

Idioma originalInglés estadounidense
Título de la publicación alojada2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509060146
DOI
EstadoPublicada - oct. 10 2018
Publicado de forma externa
Evento2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brasil
Duración: jul. 8 2018jul. 13 2018

Serie de la publicación

NombreProceedings of the International Joint Conference on Neural Networks
Volumen2018-July

Conferencia

Conferencia2018 International Joint Conference on Neural Networks, IJCNN 2018
País/TerritorioBrasil
CiudadRio de Janeiro
Período7/8/187/13/18

Áreas temáticas de ASJC Scopus

  • Software
  • Inteligencia artificial

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