Stator fault detection in induction motors by autoregressive modeling.

Garcia-Guevara Francisco M., Villalobos Piña Francisco J., Alvarez-Salas Ricardo, Cabal-Yepez Eduardo, Gonzalez-Garcia Mario A.
Mathematical Problems in Engineering, Vol. 2016, Article ID 3409756, ISSN: 1563-5147, 2016.


This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR) model.The proposed algorithmis based on instantaneous space phasor (ISP) module of stator currents, which are mapped to 𝛼-𝛽 stator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT) is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.