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February 9, 2026Open Access

Machine Learning–Based Fault Detection for Condition Monitoring of a Three-Phase Induction Motor Using Current Signature Analysis

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Authors

HMHUSSAIN MEHBOOBAHAsim HussainMCM. Abdullah Chohan

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Overview

Experimental study evaluates machine learning methods for detecting faults in induction motors, indicating efficient monitoring techniques.

Key Points

  • The aim is to identify and diagnose broken bar faults in induction motors using current signature analysis and machine learning.
  • Utilized discrete wavelet transform to extract features from stator current and voltage signals.
  • Trained various machine learning models, including SVM, KNN, and Decision Trees.
  • Evaluated models for their efficiency in diagnosing motor defects.
  • KNN classification model achieved the highest efficiency in detecting motor faults.
  • The model can differentiate between healthy and faulty motors.
  • The approach allows for timely maintenance planning based on fault percentage.

Cite This Study

MEHBOOB et al. (2026) studied this question.

synapsesocial.com/papers/698979f5f0ec2af6756e8208https://doi.org/10.5281/zenodo.18517881
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