Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM

Rafael Wilmer Contreras Urgilés; José Maldonado Ortega; Esteban Rocano; Jorge Chiluisa

doi:10.1007/978-3-031-24327-1_14

Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM

Rafael Wilmer Contreras Urgilés, José Maldonado Ortega, Esteban Rocano, Jorge Chiluisa

Grupo de Investigación en Ingeniería Del Transporte (GIIT)

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

Resumen

This paper presents the methodology applied to determine the mechanical failures in an internal combustion engine caused by the application of artificial intelligence in the classification of mechanical failures associated with the cancellation of cylinder work, that is to say this methodology is applied on the data obtained from the signal of the KS sensor (Knock Sensor) and the CMP sensor (Camshaft Position Sensor) during engine operation. To evaluate the data obtained, the acquisition of samples applied to different operating conditions is carried out, after which an attribute matrix is created that allows a selection and reduction of variables with the application of methods based on the Random Forest architecture. Subsequently, an ANN (artificial neural network) and an SVM (support vector machine) was created and trained, from which a classification error value of 0.1267% and 0.0067%, respectively, was obtained.

Idioma original	Inglés
Título de la publicación alojada	Intelligent Technologies
Subtítulo de la publicación alojada	Design and Applications for Society - Proceedings of CITIS 2022
Editores	Vladimir Robles-Bykbaev, Josefa Mula, Gilberto Reynoso-Meza
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	161-172
Número de páginas	12
ISBN (versión impresa)	9783031243264
DOI	https://doi.org/10.1007/978-3-031-24327-1_14
Estado	Publicada - 2023
Evento	8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 - Guayaquil, Ecuador Duración: 22 jun. 2022 → 24 jun. 2022

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	607 LNNS
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	8th International Conference on Science, Technology and Innovation for Society, CITIS 2022
País/Territorio	Ecuador
Ciudad	Guayaquil
Período	22/06/22 → 24/06/22

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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10.1007/978-3-031-24327-1_14

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Contreras Urgilés, R. W., Maldonado Ortega, J., Rocano, E., & Chiluisa, J. (2023). Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. En V. Robles-Bykbaev, J. Mula, & G. Reynoso-Meza (Eds.), Intelligent Technologies: Design and Applications for Society - Proceedings of CITIS 2022 (pp. 161-172). (Lecture Notes in Networks and Systems; Vol. 607 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-24327-1_14

Contreras Urgilés, Rafael Wilmer ; Maldonado Ortega, José ; Rocano, Esteban et al. / Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. Intelligent Technologies: Design and Applications for Society - Proceedings of CITIS 2022. editor / Vladimir Robles-Bykbaev ; Josefa Mula ; Gilberto Reynoso-Meza. Springer Science and Business Media Deutschland GmbH, 2023. pp. 161-172 (Lecture Notes in Networks and Systems).

@inproceedings{0a0c29be7643470b9441fcacb3c9d375,

title = "Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM",

abstract = "This paper presents the methodology applied to determine the mechanical failures in an internal combustion engine caused by the application of artificial intelligence in the classification of mechanical failures associated with the cancellation of cylinder work, that is to say this methodology is applied on the data obtained from the signal of the KS sensor (Knock Sensor) and the CMP sensor (Camshaft Position Sensor) during engine operation. To evaluate the data obtained, the acquisition of samples applied to different operating conditions is carried out, after which an attribute matrix is created that allows a selection and reduction of variables with the application of methods based on the Random Forest architecture. Subsequently, an ANN (artificial neural network) and an SVM (support vector machine) was created and trained, from which a classification error value of 0.1267% and 0.0067%, respectively, was obtained.",

keywords = "Artificial neural networks, CMP-sensor, Diagnostic, K-sensor, Mechanical failures, Support-vector machines",

author = "{Contreras Urgil{\'e}s}, {Rafael Wilmer} and {Maldonado Ortega}, Jos{\'e} and Esteban Rocano and Jorge Chiluisa",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 ; Conference date: 22-06-2022 Through 24-06-2022",

year = "2023",

doi = "10.1007/978-3-031-24327-1_14",

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isbn = "9783031243264",

series = "Lecture Notes in Networks and Systems",

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Contreras Urgilés, RW, Maldonado Ortega, J, Rocano, E & Chiluisa, J 2023, Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. En V Robles-Bykbaev, J Mula & G Reynoso-Meza (eds.), Intelligent Technologies: Design and Applications for Society - Proceedings of CITIS 2022. Lecture Notes in Networks and Systems, vol. 607 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 161-172, 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022, Guayaquil, Ecuador, 22/06/22. https://doi.org/10.1007/978-3-031-24327-1_14

Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. / Contreras Urgilés, Rafael Wilmer; Maldonado Ortega, José; Rocano, Esteban et al.
Intelligent Technologies: Design and Applications for Society - Proceedings of CITIS 2022. ed. / Vladimir Robles-Bykbaev; Josefa Mula; Gilberto Reynoso-Meza. Springer Science and Business Media Deutschland GmbH, 2023. p. 161-172 (Lecture Notes in Networks and Systems; Vol. 607 LNNS).

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

TY - GEN

T1 - Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM

AU - Contreras Urgilés, Rafael Wilmer

AU - Maldonado Ortega, José

AU - Rocano, Esteban

AU - Chiluisa, Jorge

PY - 2023

Y1 - 2023

N2 - This paper presents the methodology applied to determine the mechanical failures in an internal combustion engine caused by the application of artificial intelligence in the classification of mechanical failures associated with the cancellation of cylinder work, that is to say this methodology is applied on the data obtained from the signal of the KS sensor (Knock Sensor) and the CMP sensor (Camshaft Position Sensor) during engine operation. To evaluate the data obtained, the acquisition of samples applied to different operating conditions is carried out, after which an attribute matrix is created that allows a selection and reduction of variables with the application of methods based on the Random Forest architecture. Subsequently, an ANN (artificial neural network) and an SVM (support vector machine) was created and trained, from which a classification error value of 0.1267% and 0.0067%, respectively, was obtained.

AB - This paper presents the methodology applied to determine the mechanical failures in an internal combustion engine caused by the application of artificial intelligence in the classification of mechanical failures associated with the cancellation of cylinder work, that is to say this methodology is applied on the data obtained from the signal of the KS sensor (Knock Sensor) and the CMP sensor (Camshaft Position Sensor) during engine operation. To evaluate the data obtained, the acquisition of samples applied to different operating conditions is carried out, after which an attribute matrix is created that allows a selection and reduction of variables with the application of methods based on the Random Forest architecture. Subsequently, an ANN (artificial neural network) and an SVM (support vector machine) was created and trained, from which a classification error value of 0.1267% and 0.0067%, respectively, was obtained.

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KW - CMP-sensor

KW - Diagnostic

KW - K-sensor

KW - Mechanical failures

KW - Support-vector machines

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DO - 10.1007/978-3-031-24327-1_14

M3 - Conference contribution

AN - SCOPUS:85151049658

SN - 9783031243264

T3 - Lecture Notes in Networks and Systems

SP - 161

EP - 172

BT - Intelligent Technologies

A2 - Robles-Bykbaev, Vladimir

A2 - Mula, Josefa

A2 - Reynoso-Meza, Gilberto

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022

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ER -

Contreras Urgilés RW, Maldonado Ortega J, Rocano E, Chiluisa J. Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. En Robles-Bykbaev V, Mula J, Reynoso-Meza G, editores, Intelligent Technologies: Design and Applications for Society - Proceedings of CITIS 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 161-172. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-24327-1_14

Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM

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