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A Hybrid Multi-Experts Methodology for Mechanical Defects’ Detection and Diagnosis

Authors: Kurosh Madani 1 ; Véronique Amarger 1 and Moustapha sene 2

Affiliations: 1 PARIS-EST/ PARIS 12 University, Senart Institute of Technology, France ; 2 Gaston Berger University, Senegal

Keyword(s): None

Abstract: Compared with parametric classifiers, several advantages set Neural Networks as privileged approaches to be used as discriminating classifiers in performing diagnosis tasks. In this paper, we present a hybrid Multi-Experts neural based architecture for mechanical defects’ detection and diagnosis. This solution is evaluated within vibratory analysis frame using a wavelet transform faults’ detection scheme.

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Paper citation in several formats:
Madani K.; Amarger V.; sene M. and (2009). A Hybrid Multi-Experts Methodology for Mechanical Defects’ Detection and Diagnosis.In - Workshop ANNIIP, (ICINCO 2009) ISBN , pages 0-0. DOI: 10.5220/0002327300000000

@conference{workshop anniip09,
author={Kurosh Madani and Véronique Amarger and Moustapha sene},
title={A Hybrid Multi-Experts Methodology for Mechanical Defects’ Detection and Diagnosis},
booktitle={ - Workshop ANNIIP, (ICINCO 2009)},
year={2009},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002327300000000},
isbn={},
}

TY - CONF

JO - - Workshop ANNIIP, (ICINCO 2009)
TI - A Hybrid Multi-Experts Methodology for Mechanical Defects’ Detection and Diagnosis
SN -
AU - Madani, K.
AU - Amarger, V.
AU - sene, M.
PY - 2009
SP - 0
EP - 0
DO - 10.5220/0002327300000000

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