Detection of Bearing Faults in Induction Motor by a Combined Approach SVD-Kalman Filter
This paper presents a study on the bearing faults detection of the induction motor by a new parametric approach using the stator current signal. This technique is based on two estimators. The first extracts the faults frequencies by the singular value decomposition of the covariance matrix of the stator phase current, the second is the Kalman filter; it estimates the extent of the faults. Indeed, the main advantage of this approach is its very good frequency resolution for a very short acquisition time, something impossible to achieve with the conventional method. Moreover, in order to reduce the important computation time performed by this approach, the proposed solution consists in applying this approach only on the frequency band where the fault signature is likely to appear. Experimental results show the effectiveness of the RM method to incipient bearing fault detection.
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