The Application of BP Neural Network in Diagnosing the Short Circuit Fault of Winding

Jian Qun Zhang; De Jian Zhou

doi:10.4028/www.scientific.net/AMM.543-547.1064

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547The Application of BP Neural Network in Diagnosing...

The Application of BP Neural Network in Diagnosing the Short Circuit Fault of Winding

Abstract:

As a common fault of motor, the short circuit of rotor winding is important for the accurate diagnosis. In this article, the author collected every status parameter of motor by different sensors, using two BP neural networks to partly diagnose the motor and fusing the results of partly diagnosis by D-S evidence theory. The author increases the creditability of diagnosis results by practices and decreases uncertainty, showing the efficiency of this method.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

1064-1067

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.1064

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Cite this paper

Online since:

March 2014

Authors:

Jian Qun Zhang*, De Jian Zhou

Keywords:

BP Neural Network (BPNN), Data Fusion, DS Evidence Theory, Winding Short Circuit

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* - Corresponding Author

References

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