Neural network technique for induction motor rotor faults classification-dynamic eccentricity and broken bar faults
S Hamdani, O Touhami, R Ibtiouen… - 8th IEEE Symposium …, 2011 - ieeexplore.ieee.org
8th IEEE Symposium on Diagnostics for Electrical Machines, Power …, 2011•ieeexplore.ieee.org
This paper presents an artificial neural network (ANN) based technique to identify rotor faults
in a three-phase induction motor. The main types of faults considered are broken bar and
dynamic eccentricity. The feature extraction based on the frequency and the magnitude of
the related fault components in the stator current spectrum is performed automatically by a
Matlab script. Features with different speed and load levels are used as input for training a
feedforward layered neural network. The laboratory results show that the proposed method …
in a three-phase induction motor. The main types of faults considered are broken bar and
dynamic eccentricity. The feature extraction based on the frequency and the magnitude of
the related fault components in the stator current spectrum is performed automatically by a
Matlab script. Features with different speed and load levels are used as input for training a
feedforward layered neural network. The laboratory results show that the proposed method …
This paper presents an artificial neural network (ANN) based technique to identify rotor faults in a three-phase induction motor. The main types of faults considered are broken bar and dynamic eccentricity. The feature extraction based on the frequency and the magnitude of the related fault components in the stator current spectrum is performed automatically by a Matlab script. Features with different speed and load levels are used as input for training a feedforward layered neural network. The laboratory results show that the proposed method is able to detect the faulty conditions with high accuracy and to separate between deferent types of faults.
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