Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals

Ruben Medina; Mariela Cerrada; Diego Cabrera; Rene Vinicio Sánchez; Chuan Li; Jose Valente De Oliveira

doi:10.1109/PHM-Paris.2019.00042

Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals

Ruben Medina
, Mariela Cerrada
, Diego Cabrera
, Rene Vinicio Sánchez
, Chuan Li
, Jose Valente De Oliveira

Grupo de Investigación y Desarrollo en Tecnologías Industriales (GIDTEC)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

21 Citas (Scopus)

Resumen

A method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Editores	Chuan Li, Jose Valente de Oliveira, Ping Ding, Ping Ding, Diego Cabrera
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	210-216
Número de páginas	7
ISBN (versión digital)	9781728103297
DOI	https://doi.org/10.1109/PHM-Paris.2019.00042
Estado	Publicada - may. 2019
Evento	2019 Prognostics and System Health Management Conference, PHM-Paris 2019 - Paris, Francia Duración: 2 may. 2019 → 5 may. 2019

Serie de la publicación

Nombre	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

Conferencia

Conferencia	2019 Prognostics and System Health Management Conference, PHM-Paris 2019
País/Territorio	Francia
Ciudad	Paris
Período	2/05/19 → 5/05/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Areas de Conocimiento del CACES

827A Mantenimiento industrial

Acceder al documento

10.1109/PHM-Paris.2019.00042

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Medina, R., Cerrada, M., Cabrera, D., Sánchez, R. V., Li, C., & De Oliveira, J. V. (2019). Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. En C. Li, J. V. de Oliveira, P. Ding, P. Ding, & D. Cabrera (Eds.), Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 (pp. 210-216). Artículo 8756335 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM-Paris.2019.00042

Medina, Ruben ; Cerrada, Mariela ; Cabrera, Diego et al. / Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. editor / Chuan Li ; Jose Valente de Oliveira ; Ping Ding ; Ping Ding ; Diego Cabrera. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 210-216 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019).

@inproceedings{b5f39d504ee043859a2e0667506a3935,

title = "Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals",

abstract = "A method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 \% are obtained during cross-validation with the analyzed signals dataset.",

keywords = "acoustic emission, deep learning, Faults detection, gearbox, long short term memory networks",

author = "Ruben Medina and Mariela Cerrada and Diego Cabrera and S{\'a}nchez, \{Rene Vinicio\} and Chuan Li and \{De Oliveira\}, \{Jose Valente\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 ; Conference date: 02-05-2019 Through 05-05-2019",

year = "2019",

month = may,

doi = "10.1109/PHM-Paris.2019.00042",

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series = "Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Medina, R, Cerrada, M, Cabrera, D, Sánchez, RV, Li, C & De Oliveira, JV 2019, Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. En C Li, JV de Oliveira, P Ding, P Ding & D Cabrera (eds.), Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019., 8756335, Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019, Institute of Electrical and Electronics Engineers Inc., pp. 210-216, 2019 Prognostics and System Health Management Conference, PHM-Paris 2019, Paris, Francia, 2/05/19. https://doi.org/10.1109/PHM-Paris.2019.00042

Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. / Medina, Ruben; Cerrada, Mariela; Cabrera, Diego et al.
Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. ed. / Chuan Li; Jose Valente de Oliveira; Ping Ding; Ping Ding; Diego Cabrera. Institute of Electrical and Electronics Engineers Inc., 2019. p. 210-216 8756335 (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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AU - Medina, Ruben

AU - Cerrada, Mariela

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AU - Li, Chuan

AU - De Oliveira, Jose Valente

PY - 2019/5

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AB - A method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.

KW - acoustic emission

KW - deep learning

KW - Faults detection

KW - gearbox

KW - long short term memory networks

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BT - Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019

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Medina R, Cerrada M, Cabrera D, Sánchez RV, Li C, De Oliveira JV. Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. En Li C, de Oliveira JV, Ding P, Ding P, Cabrera D, editores, Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 210-216. 8756335. (Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019). doi: 10.1109/PHM-Paris.2019.00042