Souaidia, ChouaibThelaidjia, TawfikChenikher, Salah2025-04-212025-04-212021http://dspace.univ-oeb.dz:4000/handle/123456789/21944In 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.enRolling bearing; Fault Diagnosis; ANN; Random forest; Feature Extraction; Classification algorithmsArticle