Fault diagnosis of rolling element bearings using artificial neural network

Saadi Laribi Souad (University Ibn Khaldoun)
Bendiabdellah Azzedine (University of Sciences and Technology Mohamed Boudiaf)
Samir Meradi (National Polytechnic School Alger)

International Journal of Electrical and Computer Engineering
Vol 10, No 5: October 2020

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.