Toward Optimized Predictive Maintenance for Vehicle Systems: Deep Learning-Based Anomaly Detection Using CAN Traffic

Bournane Abbache, Mawloud Omar, Siham Bouchelaghem

2025

Abstract

This paper introduces a deep learning-based framework for predictive maintenance in vehicle systems using Controller Area Network (CAN) traffic data. Modern vehicles rely heavily on electronic components, making early fault detection crucial for ensuring safety and reliability. We propose an LSTM-based anomaly detection model that identifies irregularities in dynamic vehicle parameters, including speed, engine RPM, steering wheel angle, vehicle suspension height, and headlight position. CAN bus traffic data was meticulously extracted and preprocessed from a real vehicle prototype to train the model, which autonomously detects anomalies and potential failures. Our experimental results demonstrate the model’s effectiveness in capturing temporal dependencies within CAN data, enabling precise anomaly detection to support intelligent predictive maintenance strategies. This proactive approach minimizes downtime, enhances system reliability, and improves vehicle safety. To foster further research and collaboration, we make the generated dataset publicly available, advancing innovation in vehicle diagnostics and anomaly detection.

Paper Citation

in Harvard Style

Abbache B., Omar M. and Bouchelaghem S. (2025). Toward Optimized Predictive Maintenance for Vehicle Systems: Deep Learning-Based Anomaly Detection Using CAN Traffic. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 418-425. DOI: 10.5220/0013372500003905

in Bibtex Style

@conference{icpram25,
author={Bournane Abbache and Mawloud Omar and Siham Bouchelaghem},
title={Toward Optimized Predictive Maintenance for Vehicle Systems: Deep Learning-Based Anomaly Detection Using CAN Traffic},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={418-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013372500003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Toward Optimized Predictive Maintenance for Vehicle Systems: Deep Learning-Based Anomaly Detection Using CAN Traffic
SN - 978-989-758-730-6
AU - Abbache B.
AU - Omar M.
AU - Bouchelaghem S.
PY - 2025
SP - 418
EP - 425
DO - 10.5220/0013372500003905
PB - SciTePress