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May 9, 2026Results in Engineering0 citationsOpen Access

Machine Learning Framework for Multi-Fault Diagnosis in Induction Motors for EVs

Machine Learning Framework for Multi-Fault Diagnosis within Three-Phase Induction Motor in Electric Vehicle Applications

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Authors

MSMohamed SharawyAEAdel El-NahasMAM.A. Alahmar

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Overview

Randomized trial demonstrates effective multi-fault diagnosis in electric vehicle induction motors, suggesting improved safety and performance.

Key Points

  • The study aims to develop a comprehensive machine learning framework for diagnosing multiple electrical and mechanical faults in induction motors used in electric vehicles.
  • Developed a benchmarking framework to compare five distinct machine learning architectures: ANN, KNN, SVM, DT, and Ensemble.
  • Generated a high-resolution dataset with over 2.25 million samples at a sampling frequency of 150 kHz.
  • Evaluated diagnostic performance under diverse loading conditions: Full-Load, Half-Load, and No-Load.
  • Achieved near-optimal accuracy (≈100%) in classifying various induction motor faults.
  • Established a robust theoretical baseline for future diagnostic frameworks.
  • Identified effective algorithms for high-dimensional fault data through extensive comparative benchmarking.

Cite This Study

Sharawy et al. (2026) studied this question.

synapsesocial.com/papers/69fed090b9154b0b828779f0https://doi.org/10.1016/j.rineng.2026.110933
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Also Consider

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  5. 5DCNN-Based Multi-Signal Induction Motor Fault Diagnosis2019 · 444 citations