Use of a combined SVD-Kalman filter approach for induction motor broken rotor bars identification

Khaled Azouzi, Ahmed Hamida Boudinar, Fethi Ameur Aimer, Azeddine Bendiabdellah


This paper describes a new parametric spectral estimator for the identification of rotor bar fault of an induction motor using stator current analysis. This approach combines two methods: The first one, the Singular Value Decomposition method which allows the accurate detection and location of the fault's signature frequency. The second method allows the estimation of the fault amplitude. To this end, the Kalman filter is used for its efficient estimation of both amplitude and phase estimation using the frequencies estimated by the first method. This combination of both methods gives a better frequency resolution for a very short acquisition time, which cannot be obtained using the conventional method of the Periodogram. Moreover, in order to reduce the significant computation time resulting from the use of the Kalman filter, the proposed approach is applied only to the frequency band where the fault signature is able to appear. A series of tests will be carried out on real signals representing rotor faults.


Induction motor, Motor current signature analysis (MCSA), Broken bar, SVD, Kalman filter

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