Turn to Turn Fault Diagnosis for Induction Machines Based on Wavelet Transformation and BP Neural Network

Date

2011-09-10

Author

Najafi, Atabak
Iskender, Iris
Farhadi, Pavam
Najafi, Babak

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Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn were studied. The result showed that the method of BP NN with wavelet transformation could not only detect the exiting of the fault in stator winding, but also effectively identify the fault patterns.

Subject Keywords

Compontent, Wavelet transformation, BP network, Stator winding, Poult diagnosis

URI

https://hdl.handle.net/11511/67436

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Unclassified, Conference / Seminar

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Citation Formats

A. Najafi, I. Iskender, P. Farhadi, and B. Najafi, “Turn to Turn Fault Diagnosis for Induction Machines Based on Wavelet Transformation and BP Neural Network,” 2011, p. 294, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67436.