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Mathematical Problems in Engineering
Volume 2016, Article ID 3409756, 7 pages
Research Article

Stator Fault Detection in Induction Motors by Autoregressive Modeling

1Departamento de Ingeniería Electronica, Instituto Tecnologico de Aguascalientes, Avenida Adolfo Lopez Mateos No. 1801 Oriente, Aguascalientes, AGS, Mexico
2CIEP, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Avenida Dr. Manuel Nava No. 8, San Luis Potosí, SLP, Mexico
3Departamento de Estudios Multidisciplinarios, Division de Ingenierías Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, 38944 Yuriria, GTO, Mexico

Received 24 November 2015; Revised 2 March 2016; Accepted 8 March 2016

Academic Editor: Peter Dabnichki

Copyright © 2016 Francisco M. Garcia-Guevara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR) model. The proposed algorithm is based on instantaneous space phasor (ISP) module of stator currents, which are mapped to 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.