Article

A Modified Direct-Quadrature Axis Model for Characterization of Air-gap Mixed Eccentricity Faults in Three-Phase Induction Motor

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Abstract

Advanced signal processing techniques and high-speed analog to digital converters enabled on-line detection of internal faults of induction motor even at inception. Reliable and accurate identification of fault signatures in practical situations is always a challenge due to load oscillations, supply harmonics or the presence of multiple faults. Hence model-based analyses are essential for diagnostic studies of faults in machines. This paper proposes a modified direct and quadrature (d-q) axis based approach for modeling a three-phase squirrel cage induction motor with air-gap mixed eccentricity faults. In the proposed model, air-gap length-and thus magnetizing reactance-are modeled as a rotor position-dependent function, to represent various variable air-gap fault conditions. Stator current spectrum is used as the diagnostic signal for detection of the presence of these faults. This simple approach of modeling is computationally less intensive compared to alternative approaches such as multiple coupled circuit modeling and finite element approach. Characteristic signatures of mixed eccentricity fault obtained by simulation studies were also validated in the motor current spectrum obtained through experimentation on a motor with prefabricated eccentricity.

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... More in details, the complexity and multidisciplinary nature of the monitored systems make the FDI task on EMA particularly challenging: indeed, an acceptable level of accuracy is hardly achievable due to the interactions between different failure modes. A wide choice of FDI techniques is nowadays available in the literature: direct comparison of the system response with an appropriate monitoring model [4,5], spectral analysis of system-specific behaviours [6][7][8], artificial neural networks [9][10][11][12], or several combinations of some of these methods [13,14]. Typically, model-based approaches are more computationally expensive and require proper system knowledge but often give more accurate results than data-driven methods. ...
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