Fault Diagnosis of Induction Motors Based on RBF Neural Network

Shuo Ding; Xiao Heng Chang; Qing Hui Wu

doi:10.4028/www.scientific.net/AMM.462-463.85

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463Fault Diagnosis of Induction Motors Based on RBF...

Fault Diagnosis of Induction Motors Based on RBF Neural Network

Abstract:

In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

85-88

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.85

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Online since:

November 2013

Authors:

Shuo Ding*, Xiao Heng Chang, Qing Hui Wu

Keywords:

Artificial Neural Network (ANN), Fault Diagnosis, Induction Motor (IM), Radial Basis Function (RBF), Simulation

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* - Corresponding Author

References

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