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May 12, 2026Open Access

Machine learning–based fault detection for condition monitoring of a three-phase induction motor using current signature

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Authors

HMHUSSAIN MEHBOOBAHAsim HussainMCMuhammad Abdullah Chochan

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Overview

Randomized trial analyzes fault detection in induction motors using machine learning techniques, highlighting efficiency improvements.

Key Points

  • The aim is to analyze the effectiveness of machine learning techniques in detecting faults in induction motors using current signature analysis.
  • Analyzed broken bar faults using stator current and voltage signals.
  • Applied discrete wavelet transform (DWT) for feature extraction of motor signals.
  • Evaluated multiple machine learning models including SVM, KNN, and Decision Trees.
  • KNN classification model achieved the highest accuracy in diagnosing broken rotor bar defects.
  • The proposed model can effectively indicate motor health status and percentage of fault.
  • Maintenance planning can be optimized based on the detection of fault percentages.

Cite This Study

MEHBOOB et al. (2026) studied this question.

synapsesocial.com/papers/6a02c364ce8c8c81e9640bcbhttps://doi.org/10.24191/jeesr.v28i1.006
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