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

Abstract

This paper describes a new parametric spectral estimator for the identification of rotor bar fault of an induction motor by analyzing the stator current. 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 using the frequencies obtained 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 likely to appear. A series of tests will be carried out on real signals representing rotor faults.

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

“…In [73], a comparison of SVD and wavelet transform approaches is discussed in terms of the vector space basis. The attention of researchers has also been drawn to combined methods; for example, in [74] the authors used SVD to localize the fault frequency and then estimate it with a Kalman filter. In [75], the authors proposed an SVD method based on short-term STMS matrix series using singular value ratio (SVR), and it was shown to have positive local identification capabilities in diagnosing rolling bearing faults.…”