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Samir Meradi
National Polytechnic School Alger
Computer Science & IT Electrical & Electronics Engineering

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Fault diagnosis of rolling element bearings using artificial neural network Saadi Laribi Souad; Bendiabdellah Azzedine; Samir Meradi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1252.235 KB) | DOI: 10.11591/ijece.v10i5.pp5288-5295

Abstract

Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a Perceptron Multilayer Artificial Neural Network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects.
Co-Authors Bendiabdellah Azzedine Saadi Laribi Souad