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

Transportation Engineering Research Group (GIIT)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Intelligent Technologies
Subtitle of host publication	Design and Applications for Society - Proceedings of CITIS 2022
Editors	Vladimir Robles-Bykbaev, Josefa Mula, Gilberto Reynoso-Meza
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	161-172
Number of pages	12
ISBN (Print)	9783031243264
DOIs	https://doi.org/10.1007/978-3-031-24327-1_14
State	Published - 2023
Event	8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 - Guayaquil, Ecuador Duration: 22 Jun 2022 → 24 Jun 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	607 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	8th International Conference on Science, Technology and Innovation for Society, CITIS 2022
Country/Territory	Ecuador
City	Guayaquil
Period	22/06/22 → 24/06/22

Bibliographical note

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

Keywords

Artificial neural networks
CMP-sensor
Diagnostic
K-sensor
Mechanical failures
Support-vector machines

Access to Document

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

Cite this

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. In 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).

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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.",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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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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Contreras Urgilés RW, Maldonado Ortega J, Rocano E, Chiluisa J. Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. In Robles-Bykbaev V, Mula J, Reynoso-Meza G, editors, 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

Abstract

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Keywords

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