Stator Fault Detection in Induction Machines by Parameter Estimation, Using Adaptive Kalman Filter

In this paper first a parametric low differential order model, suitable for mathematical analysis is introduced for induction machines with faulty stator. Second, an adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous-time model with discrete measurements for fault detection ends. Typical motor faults as inter-turn short circuit and increased winding resistance are taken into account. For several decades now, there has been extensive research regarding fault detection of Induction Machines (IMs). As conventional methods of fault detection in IMs are using signal analysis methods that are based on the measurement of signals. Classical methods like Fourier and correlation analysis including FFT and spectral estimation are used to detect changes of the signal behavior caused by process faults.