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Authors: M. Benkaci 1 and G. Hoblos 2

Affiliations: 1 Institut de Recherche en Systèmes Electroniques Embarqués, France ; 2 Institut de Recherche en Systèmes Electroniques Embarqués and Ecole Supérieure d’Ingénieurs en Génie Électrique, France

Keyword(s): Leaks Detection, Automotive Diagnosis, Feature Selection, Neural Data Classification, Diesel Air Path.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Intelligent Fault Detection and Identification

Abstract: The Feature selection is an essential step for data classification used in fault detection and diagnosis process. In this work, a new approach is proposed which combines a feature selection algorithm and neural network tool for leaks detection task in diesel engine air path. The Chi2 is used as feature selection algorithm and the neural network based on Levenberg-Marquardt is used in system behaviour modelling. The obtained neural network is used for leaks detection. The model is learned and validated using data generated by xMOD. This tool is used again for test. The effectiveness of proposed approach is illustrated in simulation when the system operates on a low speed/load and the considered leak affecting the air path is very small.

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Paper citation in several formats:
Benkaci, M. and Hoblos, G. (2012). Feature Selection Combined with Neural Network for Diesel Engine Diagnosis. In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-8565-21-1; ISSN 2184-2809, SciTePress, pages 317-324. DOI: 10.5220/0004042703170324

@conference{icinco12,
author={M. Benkaci. and G. Hoblos.},
title={Feature Selection Combined with Neural Network for Diesel Engine Diagnosis},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2012},
pages={317-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004042703170324},
isbn={978-989-8565-21-1},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Feature Selection Combined with Neural Network for Diesel Engine Diagnosis
SN - 978-989-8565-21-1
IS - 2184-2809
AU - Benkaci, M.
AU - Hoblos, G.
PY - 2012
SP - 317
EP - 324
DO - 10.5220/0004042703170324
PB - SciTePress

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