A Machine learning-driven framework for predictive risk assessment of automotive air pressure systems | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Rajkumar P: Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana.India

2. Yalabaka Jahnavi: Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana.India

3. S. Poojitha: Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana.India

4. Pokala Sreeja: Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana.India

Abstract:

Air pressure systems play a critical role in modern vehicles, underpinning essential functions such as braking and suspension. Despite their importance, conventional approaches to managing air pressure system failures-primarily scheduled maintenance and manual inspections-remain largely reactive and fail to fully utilize the vast data streams generated by today's vehicles. This gap results in unplanned downtimes, increased maintenance costs, and heightened safety risks. In response, this research proposes a proactive, machine learning-based risk assessment framework that integrates real-time sensor data to predict and prevent potential failures in air pressure systems. By leveraging advanced analytics, the framework aims to provide a more accurate and forward-looking approach, reducing reliance on manual checks and enhancing overall vehicle safety and performance. Through early fault detection and predictive maintenance, the system significantly mitigates risks, curtails operational disruptions, and offers cost-effective vehicle maintenance strategies. The findings underscore the necessity of transitioning to data-driven risk assessment techniques to ensure the reliability, safety, and efficiency of air pressure systems in the automotive industry.

Page(s): 648-659

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

Keywords:

machine learning , Risk Assessment , Predictive Maintenance , Vehicle Safety , Suspension systems , Braking systems

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