Stator Fault Detection in Induction Motors by Autoregressive Modeling

Francisco M. Garcia-Guevara, Francisco J. Villalobos-Piña, Ricardo Alvarez-Salas, Eduardo Cabal-Yepez and Mario A. Gonzalez-Garcia

Mathematical Problems in Engineering, 2016, vol. 2016, 1-7

Abstract:

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3409756

DOI: 10.1155/2016/3409756

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