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

Q: How many days will it take for my paper to be published?

The review time for papers is not fixed. However, if the paper is accepted and the author completes the processing charges formalities, the paper will be published within a few working days.

Q: I would like to receive a hard copy of the journal materials. Are there any additional charges?

You can log in to the author portal and pay 500 INR to receive the hard copy materials.

Aaryan Saraswat; Dev Peshawari; Satyasheel Ray; Rajesh Kumar Agarwal

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

Authors: Aaryan Saraswat, Dev Peshawari, Satyasheel Ray, Rajesh Kumar Agarwal

Unique Paper ID: 179974
Volume: 12
Issue: 1
PageNo: 446-455

Keywords: Predictive Maintenance Automotive Diagnostics Machine Learning Deep Learning LSTM Engine Health XGBoost Real-Time Monitoring

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.

Download article

email to a friend

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

Available:https://ijirt.org/article?manuscript=179974

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now