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Predictive Maintenance of Automotive Engines Using Machine Learning and Deep Learning Techniques

  • Unique Paper ID: 179974
  • Volume: 12
  • Issue: 1
  • PageNo: 446-455
  • Abstract:
  • The integration of intelligent diagnostics in e-mobility systems is vital for ensuring vehicle reliability and minimizing operational costs. This study presents a data-driven predictive maintenance framework for electric automotive engines, utilizing real-time sensor data—including engine RPM, oil temperature, and pressure—to preemptively identify potential faults. A robust preprocessing framework was applied to address data inconsistencies, incorporating normalization, skewness correction, and correlation analysis. Four classification models were evaluated: Logistic Regression, Random Forest, XGBoost, and an LSTM neural network. The LSTM model demonstrated superior performance, achieving 95.2% accuracy and a 0.968 ROC-AUC score by effectively capturing temporal dependencies in sensor data sequences. These results underscore the potential of deep learning techniques in enabling real-time fault prediction, offering a scalable solution for reducing unplanned downtime. The proposed system aligns with IoT-enabled vehicular ecosystems, providing automotive manufacturers and fleet operators with actionable insights to optimize maintenance workflows and enhance operational efficiency.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 446-455

Predictive Maintenance of Automotive Engines Using Machine Learning and Deep Learning Techniques

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