Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches

Fault detection using motor current signature analysis (MCSA) is attractive for industrial applications due to its simplicity with no additional sensor installation required. However current components associated with faults are often very subtle and much smaller than the supply frequency component, making it challenging to detect and quantify fault levels. In this paper, we present our work on quantitative eccentricity fault diagnosis technologies for electric motors, including physical-model approach using improved winding function theory, which can simulate motor dynamics under faulty conditions and agrees well with experiment data, and data-driven approach using topological data analysis (TDA), which can effectively differentiate signals measured at different eccentricity levels. The advantages and limitations of each approach is discussed. Both methods can be extended to the detection and quantification of other types of electric motor faults.