Data-Driven Gearbox Fault Severity Diagnosis Based on Concept Drift

Mario Peña, Laura Lanzarini, Mariela Cerrada, Diego Cabrera, René Vinicio Sánchez

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

Resumen

Condition-based maintenance aims to determine the machine state in real time, by monitoring the signals it emits. Such signals are potentially unlimited, generated at a high rate, and can evolve over time. These conditions tend to produce changes in the distribution of the data, known as concept drift. This phenomenon is analyzed and used to establish changes in the state of the machine. The present article proposes a methodological framework for the diagnosis of fault severity based on concept drift. A parsimonious unsupervised algorithm based on KNN is proposed to detect concept evolution. The results show that the algorithm is quite effective in declaring a concept evolution that is associated with a change in the failure condition of the machine. Finally, the results show that there is a high correlation between the displacement of the centroids of the emerging concepts and the % of deterioration of the machine.

Idioma originalInglés
Título de la publicación alojadaETCM 2021 - 5th Ecuador Technical Chapters Meeting
EditoresMonica Karel Huerta, Sebastian Quevedo, Carlos Monsalve
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665441414
DOI
EstadoPublicada - 12 oct. 2021
Evento5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021 - Cuenca, Ecuador
Duración: 12 oct. 202115 oct. 2021

Serie de la publicación

NombreETCM 2021 - 5th Ecuador Technical Chapters Meeting

Conferencia

Conferencia5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021
País/TerritorioEcuador
CiudadCuenca
Período12/10/2115/10/21

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Publisher Copyright:
© 2021 IEEE.

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