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Stator side fault is the most common electrical failure of the induction machine and can result in a costly outage of the machine. Hence, prevention of this fault has become a major concern in the field of motor condition monitoring. Stator fault classification with ANN has been proposed in this work based on time-domain statistical features computed from the motor current signal. Mathematical model of induction motor has been used for stator fault simulation in the MATLAB platform. Two load invariant statistical features namely peak to RMS ratio and kurtosis have been computed for each phase and hence a total of 6 features have been collected as the input of the proposed ANN-based fault classifier. A severity index has also been proposed based on the negative sequence component of stator current.

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ANN-Based Motor Current Signature Analysis for Induction Motor Stator Fault Diagnosis

  • 会议论文

  • 发表日期：

    2026年

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  • Sudip Halder,
  • Bimal Dora,
  • Sunil Bhat

摘要

Stator side fault is the most common electrical failure of the induction machine and can result in a costly outage of the machine. Hence, prevention of this fault has become a major concern in the field of motor condition monitoring. Stator fault classification with ANN has been proposed in this work based on time-domain statistical features computed from the motor current signal. Mathematical model of induction motor has been used for stator fault simulation in the MATLAB platform. Two load invariant statistical features namely peak to RMS ratio and kurtosis have been computed for each phase and hence a total of 6 features have been collected as the input of the proposed ANN-based fault classifier. A severity index has also been proposed based on the negative sequence component of stator current.