Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals
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Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, Algeria.
Department of Electrical Engineering, Mohamed Cherif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria
lakehal Abdelaziz   

Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, Algeria.
Submission date: 2020-07-31
Final revision date: 2021-01-09
Acceptance date: 2021-02-05
Online publication date: 2021-02-09
Publication date: 2021-03-04
Diagnostyka 2021;22(1):65–74
Installations and the detection of their faults has become a major challenge. In order to develop a reliable approach for monitoring and diagnosis faults of these components, a test rig was mounted. In this article, a Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) has been structured and optimized for online monitoring of induction motors. The input layer of our ANN used eight indicators calculated from the collected time signals and which represent the different states of the motor (Healthy, broken rotor bars, bearing fault and Misalignment) and the output layer used a codified matrix. However, based on L27 Taguchi design, the architecture for the hidden layers of our network is chosen, with the use of the Levenberg-Marquardt learning algorithm. Garson's algorithm and connection weight approach showed that there's a great sensitivity of the crest factor, the kurtosis and the variance on the effectiveness of our diagnostic system. Consequently, the obtained results are capable of detecting faults in the induction motor under different operating conditions.
The authors would like to thank the Directorate General for Scientific Research and Technological Development (DGRSDT) for financial support (PRFU code: A01L09UN410120190002). Also, they would like to express their appreciation for the valuable time that anonymous reviewers have dedicated to the review process.
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