Developing an Intelligent Pattern Using Principal Component Analysis Method to Detect Eccentricity Faults in Squirrel-Cage Induction Motors

Eccentricity fault is one of the prevalent faults in rotating machines which can cause other mechanical and electrical faults. This paper focuses on detecting the static and dynamic eccentricity faults in the squirrel-cage induction motors. The induction motor is modeled by the finite element method (FEM), a powerful and accurate method, using FLUX 2D software. The current signal cannot be used to detect the static and dynamic eccentricity faults especially when their severity is small. Therefore, the search-coil based method is employed and the voltage of two symmetrical search-coils is analyzed to detect and diagnose the static and dynamic faults. Since two search-coils are open-circuit, they do not affect the behavior of the induction motor. The analysis based on the principal component analysis (PCA) method shows that there exists an intelligent pattern that, firstly, is sensitive to the occurrence of eccentricity faults, even to a low degree, and secondly, has the ability to distinguish the type of fault (static or dynamic).