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A comparative Study for Ball Bearing Fault Classification Using Kernel-SVM with Kullback Leibler Divergence Selected Features

Zahra Mezni 1 Claude Delpha 2 Demba Diallo 3 Ahmed Braham 4 
1 Université de Carthage - University of Carthage
2 L2S - Laboratoire des signaux et systèmes
3 GeePs - Laboratoire Génie électrique et électronique de Paris
4 INSAT Tunis
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02295400
Contributor : Claude Delpha Connect in order to contact the contributor
Submitted on : Tuesday, September 24, 2019 - 10:56:07 AM
Last modification on : Saturday, June 25, 2022 - 10:39:43 PM

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CNRS | SUP_LSS | SUP_SIGNAUX | CENTRALESUPELEC | GEEPS | ECO2 | UNIV-PARIS-SACLAY | SORBONNE-UNIVERSITE | SU-SCIENCES | GS-ENGINEERING | GS-COMPUTER-SCIENCE | INSTITUT-SCIENCES-LUMIERE

Citation

Zahra Mezni, Claude Delpha, Demba Diallo, Ahmed Braham. A comparative Study for Ball Bearing Fault Classification Using Kernel-SVM with Kullback Leibler Divergence Selected Features. IEEE International Conference on Industrial Electronics (IEEE IECON 2019), Oct 2019, Lisbonne, Portugal. ⟨10.1109/iecon.2019.8926731⟩. ⟨hal-02295400⟩

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