Condition classification for bearing fault based on machine learning using GUI
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oum El Bouaghi
Abstract
In 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.
Description
Keywords
Rolling bearing; Fault Diagnosis; ANN; Random forest; Feature Extraction;
Classification algorithms