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

Kurosh Madani, Véronique Amarger, Moustapha sene

2009

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.

References

  1. N. Tandon, A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings”, Trib. Internat. 32, p. 469-480, (1999).
  2. S. P. Harsha, “Non linear dynamic response of a balanced rotor supported on rolling element bearing”, Elsevier, Apr. (2004)
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Paper Citation


in Harvard Style

Madani K., Amarger V. and sene M. (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


in Bibtex Style

@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={},
}


in EndNote Style

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