Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals
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.
Data nadesłania: 31-07-2020
Data ostatniej rewizji: 09-01-2021
Data akceptacji: 05-02-2021
Data publikacji online: 09-02-2021
Data publikacji: 04-03-2021
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.
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