Condition classification for bearing fault based on machine learning using GUI

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dc.contributor.authorSouaidia, Chouaib
dc.contributor.authorThelaidjia, Tawfik
dc.contributor.authorChenikher, Salah
dc.date.accessioned2025-04-21T15:42:44Z
dc.date.available2025-04-21T15:42:44Z
dc.date.issued2021
dc.description.abstractIn this paper, a new method based on a graphical user interface is developed for bearing fault diagnosis. The suggested method consists of using statistical parameters for feature extraction. Then the classification task is guaranteed using two classifiers namely: neural networks and random forest. The suggested approach is tested based on the bearing dataset provided by the Case Western Reserve University, Bearing Data Center. To facilitate the exploitation of the proposed approach a GUI has been developed. The obtained results show the effectiveness and the simplicity of the proposed approach for bearing fault diagnosis.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/21944
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.subjectRolling bearing; Fault Diagnosis; ANN; Random forest; Feature Extraction; Classification algorithms
dc.titleCondition classification for bearing fault based on machine learning using GUI
dc.typeArticle
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The 1 ST international conference on electronics, artificial intelligence and technologies ICEAINT 2021