Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor

Besma Bessam (), Arezki Menacer (), Mohamed Boumehraz () and Hakima Cherif ()
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Besma Bessam: University of Biskra
Arezki Menacer: University of Biskra
Mohamed Boumehraz: University of Biskra
Hakima Cherif: University of El-Oued

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 1, No 44, 478-488

Abstract: Abstract It is well known that stator winding faults such the inter-turn short circuit are the most frequent source of breakdowns in induction motors. Early detection of any small inter-turn short circuit and location of the faulty phase at different load would eliminate some subsequent damage to adjacent coils and stator core, reducing then the repair cost. To achieve this purpose, the present paper presents a new method of diagnosis and detection of inter turn short circuit fault using discrete wavelet transform (DWT) and neural networks (NN). This method consists in analyzing the stator current by DWT in order to compute the energy associated with the stator fault in the frequency bandwidth. Then, this energy is used as input for a NN classifier. The results obtained are astonishing and the approach is able to detect any small number of shorted turns and the faulty phase even under different load of the machine.

Keywords: Diagnosis; Induction motors (IM); Stator fault; Inter-turn-short circuit; Discrete wavelet transform (DWT); Neural network (NN) (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-015-0400-4

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