On-line detection of stator and rotor faults occurring in induction machine diagnosis by parameters estimation

The authors propose a diagnosis method for on-line interturns short-circuit windings and broken bars detection by parameters estimation. For predictive detection, Kalman filtering algorithm has been adapted to take into account the on-line parameters deviations in faulty case. Experimental rig is us...

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Bibliographic Details
Published in:	8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives pp. 105 - 112
Main Authors:	Bazine, I. B. A., Tnani, S., Poinot, T., Champenois, G., Jelassi, K.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-09-2011
Subjects:	Bars broken rotor bars Circuit faults Diagnosis Flux2D induction motor Induction motors Kalman filtering on-line parameter estimation Rotors short-circuit Stator windings Windings
Online Access:	Get full text
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Summary:	The authors propose a diagnosis method for on-line interturns short-circuit windings and broken bars detection by parameters estimation. For predictive detection, Kalman filtering algorithm has been adapted to take into account the on-line parameters deviations in faulty case. Experimental rig is used to validate the on-line identification of stator default. Within the framework of the rotor defects diagnosis, it is difficult to conduct experimental tests to validate the on-line identification of such default. For this reason, one propose an on-line technique to detect rotor broken bars. This technique was validated by using a finite element software (Flux2D). Estimation results show a good agreement and demonstrate the possibility of on-line stator and rotor faults detection.
ISBN:	1424493013 9781424493012
DOI:	10.1109/DEMPED.2011.6063609