Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor

Ewert, Paweł

doi:10.2478/pead-2019-0001

Abstract

The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB–Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.

References

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Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor

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