Abstract
In this paper, a novel model-based strategy for bearing fault detection in induction motor is presented. The proposed method is based on state estimation of induction motor using discrete-time extended kalman filter. Non-invasive fault detection is the critical advantage of the proposed strategy. The method is robust towards unbalanced supply and variable speed. Analytical computations are carried on 4 hp squirrel-cage induction motor using MATLAB software for different bearing faults. The presented simulation results are validated using auto correlation coefficients. Additionally, the proposed strategy provides better estimates for all types of bearing faults.
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