Pattern identification for eccentricity fault diagnosis in permanent magnet synchronous motors using stator current monitoring

A novel theoretical scrutiny is presented here, which extracts eccentricity fault signatures by monitoring the stator current in permanent magnet synchronous motors (PMSMs). In this analysis, effects of the stator slots and saturation are taken into account for static and dynamic eccentricity fault detection. Eccentricity signatures are utilised to introduce a particular frequency pattern, and amplitude of side-band components at proposed frequencies is employed as a proper index for eccentricity fault recognition. Competence of the nominated index to detect eccentricity, its type and its degree is investigated in faulty PMSM with different load levels. Hence, time-stepping finite-element method is used to model faulty PMSM and calculate stator currents for processing and obtaining aforementioned proposed index. The relation between the nominated index, static and dynamic eccentricity degrees is determined by a mutual information criterion. So, a white Gaussian noise is added to the simulated current and robustness of the proposed index is analysed with respect to the noise variance. Finally, the type and the degree of eccentricity are predicted using support vector machine as a classifier. The classification results indicate that the proposed features can estimate the eccentricity degree and its type. The simulation results are verified by the experimental results.