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

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Intelligent Technologies: Design and Applications for Society (CITIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 607))

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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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Authors and Affiliations

  1. Universidad Politécnica Salesiana, Cuenca, Ecuador

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

Authors
  1. Rafael Wilmer Contreras Urgilés
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  2. José Maldonado Ortega
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  3. Esteban Rocano
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  4. Jorge Chiluisa
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Corresponding author

Correspondence to Rafael Wilmer Contreras Urgilés .

Editor information

Editors and Affiliations

  1. Campus El Vecino, Universidad Politécnica Salesiana, Cuenca, Ecuador

    Vladimir Robles-Bykbaev

  2. Research Centre on Production Management and Engineering - CIGIP, Universitat Politècnica de València, Alcoy, Spain

    Josefa Mula

  3. Rua Imaculada Conceiçao, Pontifícia Universidade Católica do Paraná – PUCPR, Curitiba, Brazil

    Gilberto Reynoso-Meza

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Contreras Urgilés, R.W., Ortega, J.M., Rocano, E., Chiluisa, J. (2023). Classification of Mechanical Failures in Provoked Ignition Engine by Means of ANN and SVM. In: Robles-Bykbaev, V., Mula, J., Reynoso-Meza, G. (eds) Intelligent Technologies: Design and Applications for Society. CITIS 2022. Lecture Notes in Networks and Systems, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-031-24327-1_14

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