- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/1840889.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
induction motor
vibration analysis
monitoring
diagnosis
optimized artificial neural network
sensitivity analysis
silnik indukcyjny
analiza drgań
monitorowanie
diagnoza
sztuczna sieć neuronowa
analiza wrażliwości - Opis:
- 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.
- Źródło:
-
Diagnostyka; 2021, 22, 1; 65-74
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł