Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning

Pablo Lucero; Réne Vinicio Sánchez; Jean Carlo MacAncela; Diego Cabrera; Mariela Cerrada; Chuan Li; Higinio Rubio Alonso

doi:10.1109/PHM-Paris.2019.00056

Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning

Pablo Lucero
, Réne Vinicio Sánchez
, Jean Carlo MacAncela
, Diego Cabrera
, Mariela Cerrada
, Chuan Li
, Higinio Rubio Alonso

Grupo de Investigación y Desarrollo en Tecnologías Industriales (GIDTEC)

Producción científica: Contribución a una conferencia › Capítulo

Resumen

In this paper, a methodology for accelerometer placement comparison for crack detection in railway axles, using vibration signals and machine learning, was shown. Different vibration signals from six accelerometers were obtained by several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm. This paper describes three stages: acquisition, processing, and analysis. The findings suggest that using the vertical or longitudinal accelerometer located in left allow obtaining higher accuracy than 90% with three features, also called condition indicators. On the other hand, an accuracy such as 96.43% is obtained using a left vertical sensor and 95,98% using a left longitudinal sensor, both with ten features. With this methodology, high accuracy in crack detection was obtained using an accelerometer effective placement. Different vibration signals using six accelerometers were obtained, under several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm.

Idioma original	Inglés
Páginas	291-296
Número de páginas	6
DOI	https://doi.org/10.1109/PHM-Paris.2019.00056
Estado	Publicada - 12 jul 2019
Evento	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 - Duración: 1 may 2019 → …

Conferencia

Conferencia	Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Período	1/05/19 → …

Areas de Conocimiento del CACES

827A Mantenimiento industrial

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10.1109/PHM-Paris.2019.00056

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Monitoreo Inteligente de la Condición de Maquinaria Rotativa Mediante la Fusión de Señales de Audio, Emisión Acústica, Vibración y Corriente
Llerena Pizarro, O. R. (Investigador Secundario), Sanchez Loja, R. V. (Investigador principal), Cabrera Mendieta, D. R. (Investigador Secundario), Lucero Otorongo, P. M. (Investigador Externo), Macancela Poveda, J. C. (Investigador Externo), Perez Rivera, I. A. (Investigador Externo), Pacheco Cordova, E. E. (Investigador Externo), Vacacela Costa, A. S. (Estudiante Investigador), Pacheco Montilla, F. K. (Investigador Externo), Villacis Marin, M. L. (Investigador Secundario), Guaman Buestan, A. D. P. (Investigador Secundario), Torres Diaz, C. P. (Investigador Externo), Valente De Oliveira, J. L. (Investigador Externo), Vásquez, R. (Investigador Externo), Lojano Armijos, F. J. (Estudiante Investigador), Chingal Imaicela, D. E. (Investigador Externo), Siguencia Urgiles, J. F. (Investigador Externo), Cajas Muñoz, F. D. (Investigador Externo), Montalvan Pulla, F. I. (Estudiante Investigador), Quinteros Espinoza, M. E. (Investigador Externo), Ortega Lucero, L. R. (Estudiante Investigador), Llivicura Orellana, H. F. (Estudiante Investigador), Calle Lazo, A. K. (Estudiante Investigador) & Li, C. (Investigador Externo)
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Proyecto: Investigación y Desarrollo

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@conference{18f1b3c1b88b4b83a92d548cf0100b22,

title = "Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning",

abstract = "In this paper, a methodology for accelerometer placement comparison for crack detection in railway axles, using vibration signals and machine learning, was shown. Different vibration signals from six accelerometers were obtained by several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm. This paper describes three stages: acquisition, processing, and analysis. The findings suggest that using the vertical or longitudinal accelerometer located in left allow obtaining higher accuracy than 90\% with three features, also called condition indicators. On the other hand, an accuracy such as 96.43\% is obtained using a left vertical sensor and 95,98\% using a left longitudinal sensor, both with ten features. With this methodology, high accuracy in crack detection was obtained using an accelerometer effective placement. Different vibration signals using six accelerometers were obtained, under several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm.",

keywords = "crack detection, feature selection, machine learning, railway, vibration signal",

author = "Pablo Lucero and S{\'a}nchez, \{R{\'e}ne Vinicio\} and MacAncela, \{Jean Carlo\} and Diego Cabrera and Mariela Cerrada and Chuan Li and Alonso, \{Higinio Rubio\}",

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AU - Lucero, Pablo

AU - Sánchez, Réne Vinicio

AU - MacAncela, Jean Carlo

AU - Cabrera, Diego

AU - Cerrada, Mariela

AU - Li, Chuan

AU - Alonso, Higinio Rubio

PY - 2019/7/12

Y1 - 2019/7/12

N2 - In this paper, a methodology for accelerometer placement comparison for crack detection in railway axles, using vibration signals and machine learning, was shown. Different vibration signals from six accelerometers were obtained by several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm. This paper describes three stages: acquisition, processing, and analysis. The findings suggest that using the vertical or longitudinal accelerometer located in left allow obtaining higher accuracy than 90% with three features, also called condition indicators. On the other hand, an accuracy such as 96.43% is obtained using a left vertical sensor and 95,98% using a left longitudinal sensor, both with ten features. With this methodology, high accuracy in crack detection was obtained using an accelerometer effective placement. Different vibration signals using six accelerometers were obtained, under several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm.

AB - In this paper, a methodology for accelerometer placement comparison for crack detection in railway axles, using vibration signals and machine learning, was shown. Different vibration signals from six accelerometers were obtained by several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm. This paper describes three stages: acquisition, processing, and analysis. The findings suggest that using the vertical or longitudinal accelerometer located in left allow obtaining higher accuracy than 90% with three features, also called condition indicators. On the other hand, an accuracy such as 96.43% is obtained using a left vertical sensor and 95,98% using a left longitudinal sensor, both with ten features. With this methodology, high accuracy in crack detection was obtained using an accelerometer effective placement. Different vibration signals using six accelerometers were obtained, under several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm.

KW - crack detection

KW - feature selection

KW - machine learning

KW - railway

KW - vibration signal

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Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning

Resumen

Conferencia

Areas de Conocimiento del CACES

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Proyectos

Monitoreo Inteligente de la Condición de Maquinaria Rotativa Mediante la Fusión de Señales de Audio, Emisión Acústica, Vibración y Corriente

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