Publicado

2023-03-03

Vehicle maintenance management based on machine learning in agricultural tractor engines

Gestión del mantenimiento de vehículos basada en el aprendizaje autónomo en motores de tractores agrícolas

DOI:

https://doi.org/10.15446/dyna.v90n225.103612

Palabras clave:

autonomous learning; classification algorithm; predictive maintenance; vibrations (en)
aprendizaje autónomo; algoritmo de clasificación; mantenimiento predictivo; vibraciones (es)

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Autores/as

The objective of this work is to use the autonomous learning methodology as a tool in vehicle maintenance management. In obtaining data, faults in the fuel supply system have been simulated, causing anomalies in the combustion process that are easily detectable by vibrations obtained from a sensor in the engine of an agricultural tractor. To train the classification algorithm, 4 engine states were used: BE (optimal state), MEF1, MEF2, MEF3 (simulated failures). The applied autonomous learning is of the supervised type, where the samples were initially characterized and labeled to create a database for the execution of the training. The results show that the training carried out within the classification algorithm has an efficiency greater than 90%, which indicates that the method used is applicable in the management of vehicle maintenance to predict failures in engine operation.

El objetivo del trabajo es utilizar la metodología de aprendizaje autónomo como herramienta en la gestión del mantenimiento de vehículos. En la obtención de datos se han simulado fallos en el sistema de alimentación de combustible que provocan anomalías en el proceso de combustión que son fácilmente detectables por vibraciones obtenidas de un sensor en el motor de un tractor agrícola. Para entrenar el algoritmo de clasificación se utilizaron 4 estados del motor: BE (estado óptimo), MEF1, MEF2, MEF3 (fallas simuladas). El aprendizaje autónomo aplicado es del tipo supervisado, donde inicialmente se caracterizó y rotuló las muestras para crear una base de datos para la ejecución del entrenamiento. Los resultados muestran que el entrenamiento realizado dentro del algoritmo de clasificación tiene una eficiencia superior al 90%, lo que indica que el método utilizado es aplicable en la gestión del mantenimiento de vehículos para predecir fallas en el funcionamiento del motor.

Referencias

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Cómo citar

IEEE

[1]
C. N. Mafla-Yépez, C. F. Morales-Bayetero, E. P. Hernández-Rueda, y I. B. Benavides-Cevallos, «Vehicle maintenance management based on machine learning in agricultural tractor engines », DYNA, vol. 90, n.º 225, pp. 22–28, mar. 2023.

ACM

[1]
Mafla-Yépez, C.N., Morales-Bayetero, C.F., Hernández-Rueda, E.P. y Benavides-Cevallos, I.B. 2023. Vehicle maintenance management based on machine learning in agricultural tractor engines . DYNA. 90, 225 (mar. 2023), 22–28. DOI:https://doi.org/10.15446/dyna.v90n225.103612.

ACS

(1)
Mafla-Yépez, C. N.; Morales-Bayetero, C. F.; Hernández-Rueda, E. P.; Benavides-Cevallos, I. B. Vehicle maintenance management based on machine learning in agricultural tractor engines . DYNA 2023, 90, 22-28.

APA

Mafla-Yépez, C. N., Morales-Bayetero, C. F., Hernández-Rueda, E. P. & Benavides-Cevallos, I. B. (2023). Vehicle maintenance management based on machine learning in agricultural tractor engines . DYNA, 90(225), 22–28. https://doi.org/10.15446/dyna.v90n225.103612

ABNT

MAFLA-YÉPEZ, C. N.; MORALES-BAYETERO, C. F.; HERNÁNDEZ-RUEDA, E. P.; BENAVIDES-CEVALLOS, I. B. Vehicle maintenance management based on machine learning in agricultural tractor engines . DYNA, [S. l.], v. 90, n. 225, p. 22–28, 2023. DOI: 10.15446/dyna.v90n225.103612. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/103612. Acesso em: 13 mar. 2026.

Chicago

Mafla-Yépez, Carlos Nolasco, Cesar Fabricio Morales-Bayetero, Erik Paul Hernández-Rueda, y Ignacio Bayardo Benavides-Cevallos. 2023. «Vehicle maintenance management based on machine learning in agricultural tractor engines ». DYNA 90 (225):22-28. https://doi.org/10.15446/dyna.v90n225.103612.

Harvard

Mafla-Yépez, C. N., Morales-Bayetero, C. F., Hernández-Rueda, E. P. y Benavides-Cevallos, I. B. (2023) «Vehicle maintenance management based on machine learning in agricultural tractor engines », DYNA, 90(225), pp. 22–28. doi: 10.15446/dyna.v90n225.103612.

MLA

Mafla-Yépez, C. N., C. F. Morales-Bayetero, E. P. Hernández-Rueda, y I. B. Benavides-Cevallos. «Vehicle maintenance management based on machine learning in agricultural tractor engines ». DYNA, vol. 90, n.º 225, marzo de 2023, pp. 22-28, doi:10.15446/dyna.v90n225.103612.

Turabian

Mafla-Yépez, Carlos Nolasco, Cesar Fabricio Morales-Bayetero, Erik Paul Hernández-Rueda, y Ignacio Bayardo Benavides-Cevallos. «Vehicle maintenance management based on machine learning in agricultural tractor engines ». DYNA 90, no. 225 (marzo 3, 2023): 22–28. Accedido marzo 13, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/103612.

Vancouver

1.
Mafla-Yépez CN, Morales-Bayetero CF, Hernández-Rueda EP, Benavides-Cevallos IB. Vehicle maintenance management based on machine learning in agricultural tractor engines . DYNA [Internet]. 3 de marzo de 2023 [citado 13 de marzo de 2026];90(225):22-8. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/103612

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CrossRef citations2

1. Bernardo Tormos, Benjamín Pla, Ramón Sánchez-Márquez, Jose Luis Carballo. (2025). Explainable AI Using On-Board Diagnostics Data for Urban Buses Maintenance Management: A Study Case. Information, 16(2), p.74. https://doi.org/10.3390/info16020074.

2. El Khiate Mounir, Samri Hassan, Samri Hassan, Znaidi Zineb. (2024). An Interface Development based on Internet of Things Approach for Smart Predictive Maintenance implementation: Case of Diesel Engine. 2024 International Conference on Circuit, Systems and Communication (ICCSC). , p.1. https://doi.org/10.1109/ICCSC62074.2024.10616750.

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