Diagnosis of rotor fault in induction motor using the MUSIC analysis of the terminal voltage after switch-off

Hamdani, S.; Bouzida, Ahcene; Touhami, O.; Ibtiouen, R.

doi:10.1109/icelmach.2008.4799985

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The machines can be supplied by the main or by an industrial inverter. The current is measured using LA-55P Hall-effect current sensor and simultaneously sampled through channels of 16 [20]. Using this setup, the three motors were tested for six load levels (from 10 to 80%) and three power supply frequencies (50, 40 and 25 Hz), giving a total of 102 tests.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Neural network technique for induction motor rotor faults classification-dynamic eccentricity and broken bar faults-

Hamdani

Touhami

Ibtiouen

et al. 2011

8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics &Amp; Drives

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Φ Abstract --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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Section: A Data Acquisitionmentioning

confidence: 99%