Fault Diagnosis by Bayesian Network Classifiers with a Distance Rejection Criterion

M. Atoui, Achraf Cohen, Phillipe Rauffet, Pascal Berruet

Abstract

In this paper, Bayesian network classifiers (BNCs) are used as a statistical tool to diagnosis faults with a distance rejection criterion. The proposed approach enhances significantly the structure of the use of Bayesian networks in the same context. Our framework is evaluated and compared to state of the art using data from the benchmark Tennessee Eastman Process (TEP).

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Paper Citation


in Harvard Style

Atoui M., Cohen A., Rauffet P. and Berruet P. (2019). Fault Diagnosis by Bayesian Network Classifiers with a Distance Rejection Criterion.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 463-468. DOI: 10.5220/0008053304630468


in Bibtex Style

@conference{icinco19,
author={M. Atoui and Achraf Cohen and Phillipe Rauffet and Pascal Berruet},
title={Fault Diagnosis by Bayesian Network Classifiers with a Distance Rejection Criterion},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={463-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008053304630468},
isbn={978-989-758-380-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Fault Diagnosis by Bayesian Network Classifiers with a Distance Rejection Criterion
SN - 978-989-758-380-3
AU - Atoui M.
AU - Cohen A.
AU - Rauffet P.
AU - Berruet P.
PY - 2019
SP - 463
EP - 468
DO - 10.5220/0008053304630468