Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals

• Autorzy: Khoualdia Tarek , Lakehal Abdelaziz , Chelli Zoubir , Khoualdia Kais , Nessaib Karim

Identyfikatory

DOI

10.29354/diag/133091

• Języki publikacji: EN

Abstrakty

EN

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 LevenbergMarquardt 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.

Słowa kluczowe

EN

induction motor; vibration analysis; monitoring; diagnosis; optimized artificial neural network; sensitivity analysis

PL

silnik indukcyjny; analiza drgań; monitorowanie; diagnoza; sztuczna sieć neuronowa; analiza wrażliwości

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 1
• Strony: 65--74
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Khoualdia Tarek

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

autor

Lakehal Abdelaziz

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

autor

Chelli Zoubir

• Department of Electrical Engineering, Mohamed Cherif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

autor

Khoualdia Kais

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

autor

Nessaib Karim

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).