Gear Crack Level Classification by Using KNN and Time-Domain Features from Acoustic Emission Signals under Different Motor Speeds and Loads

Rene Vinicio Sanchez, Pablo Lucero, Jean Carlo Macancela, Mariela Cerrada, Diego Cabrera, Rafael Vasquez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

Diagnosing failures during their initial stage is important to avoid unexpected stops and catastrophic damages, specially for gear boxes that are crucial components in industrial machines. This work addresses the classification of nine levels of crack failure severity in a gearbox. First of all, features are extracted in time domain from signals coming from an acoustic emission (AE) sensor, and then selected by using four different ranking methods. The classification stage uses the k-Nearest Neighbors (KNN) technique. The results indicate that presented levels of severity can be successfully classified with five features extracted from the AE signal for the four ranking methods.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
EditoresDian Wang, Yong Zhou, Diego Cabrera, Chuan Li, Chunlin Zhang
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas465-470
Número de páginas6
ISBN (versión digital)9781538660577
DOI
EstadoPublicada - 11 mar. 2019
Publicado de forma externa
Evento2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 - Xi'an, China
Duración: 15 ago. 201817 ago. 2018

Serie de la publicación

NombreProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018

Conferencia

Conferencia2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
País/TerritorioChina
CiudadXi'an
Período15/08/1817/08/18

Nota bibliográfica

Publisher Copyright:
© 2018 IEEE.

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