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Enhancing Automotive Safety and Maintenance: An Intelligent Fault Detection Approach Using Fuzzy Logic
Subject area: Science,Engineering and Technology · Area of research: Computer Science
DOI: https://doi.org/10.64388/IREV9I8-1714331
Abstract
The rise of intelligent systems in the automotive industry has paved the way for significant advancements in vehicle safety and maintenance. This paper presents an innovative fault detection system that integrates fuzzy logic with Support Vector Machine (SVM) to enhance automotive diagnostics. The system leverages fuzzy logic to handle imprecise and uncertain sensor data, providing an initial diagnosis that is refined by the SVM model through data-driven learning. The proposed hybrid system demonstrates high accuracy, precision, recall, and F1 score, outperforming traditional diagnostic methods and comparable advanced systems. A comprehensive methodology is detailed, including the hardware and software requirements, data preprocessing, feature selection, and the implementation of fuzzy logic principles and Mamdani's algorithm. The combination of these components ensures robust performance and scalability. The system’s capabilities are evaluated through performance metrics and comparative analysis, with results presented in both tabular and visual formats. Case studies further illustrate the system's effectiveness in real-world scenarios, highlighting its ability to prevent significant mechanical failures and reduce maintenance costs. The findings suggest that the intelligent fault detection system not only enhances diagnostic accuracy and reliability but also contributes to proactive vehicle maintenance, thereby improving overall automotive safety. This research underscores the potential of integrating fuzzy logic with machine learning techniques in developing advanced diagnostic tools, setting a new benchmark for automotive maintenance practices. The system's real-time diagnostics and remote monitoring capabilities, facilitated by IoT integration and secure internet connectivity, further emphasize its practical applications in modern vehicular environments.
Keywords
Intelligent Fault Detection; Fuzzy Logic; Support Vector Machine; Automotive Diagnostics; Real-Time Monitoring; Proactive Maintenance
References
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[2] Boshra, M., & Al-Sharhan, S. (2021). Fuzzy logic and machine learning for vehicle fault diagnosis. International Journal of Advanced Computer Science and Applications, 12(3), 107-114. https://doi.org/10.14569/IJACSA.2021.0120313
[3] Davis, R., & Thomas, J. (2020). Comparative analysis of traditional and intelligent automotive diagnostic systems. Journal of Vehicle Engineering, 18(2), 85-98. https://doi.org/10.1016/j.jve.2020.06.003
[4] Fogel, E., & Huang, X. (2018). Application of machine learning techniques in vehicle fault diagnosis. IEEE Access, 6, 17050-17059. https://doi.org/10.1109/ACCESS.2018.2817118
[5] Gupta, R., Mehra, R., & Singh, A. (2019). Hybrid intelligent systems for automotive fault detection: A review. Journal of Artificial Intelligence Research, 65, 289-310. https://doi.org/10.1613/jair.1.11250
[6] Kumar, V., & Singh, P. (2019). Enhancing automotive maintenance with intelligent fault detection systems. Journal of Intelligent Transportation Systems, 23(4), 450-462. https://doi.org/10.1080/15472450.2019.1594385
[7] Lee, C., & Park, H. (2022). Applications of fuzzy logic in automotive diagnostics: A review. Expert Systems with Applications, 187, 115897. https://doi.org/10.1016/j.eswa.2021.115897
[8] Martin, G., & Wang, X. (2018). Machine learning techniques for predictive maintenance in the automotive industry. Journal of Machine Learning Research, 19(1), 523-547. http://jmlr.org/papers/v19/17-716.html
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How to cite this paper
@article{1714331,
author = {Ayeh Blessing Elohor, Asheshemi Nelson Oghenekevwe, Michael Adawaren, Batse E. Taji},
title = {Enhancing Automotive Safety and Maintenance: An Intelligent Fault Detection Approach Using Fuzzy Logic},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {8},
pages = {868-876},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714331.pdf},
abstract = {The rise of intelligent systems in the automotive industry has paved the way for significant advancements in vehicle safety and maintenance. This paper presents an innovative fault detection system that integrates fuzzy logic with Support Vector Machine (SVM) to enhance automotive diagnostics. The system leverages fuzzy logic to handle imprecise and uncertain sensor data, providing an initial diagnosis that is refined by the SVM model through data-driven learning. The proposed hybrid system demonstrates high accuracy, precision, recall, and F1 score, outperforming traditional diagnostic methods and comparable advanced systems. A comprehensive methodology is detailed, including the hardware and software requirements, data preprocessing, feature selection, and the implementation of fuzzy logic principles and Mamdani's algorithm. The combination of these components ensures robust performance and scalability. The system’s capabilities are evaluated through performance metrics and comparative analysis, with results presented in both tabular and visual formats. Case studies further illustrate the system's effectiveness in real-world scenarios, highlighting its ability to prevent significant mechanical failures and reduce maintenance costs. The findings suggest that the intelligent fault detection system not only enhances diagnostic accuracy and reliability but also contributes to proactive vehicle maintenance, thereby improving overall automotive safety. This research underscores the potential of integrating fuzzy logic with machine learning techniques in developing advanced diagnostic tools, setting a new benchmark for automotive maintenance practices. The system's real-time diagnostics and remote monitoring capabilities, facilitated by IoT integration and secure internet connectivity, further emphasize its practical applications in modern vehicular environments.},
keywords = {Intelligent Fault Detection; Fuzzy Logic; Support Vector Machine; Automotive Diagnostics; Real-Time Monitoring; Proactive Maintenance},
month = {February},
doi = {https://doi.org/10.64388/IREV9I8-1714331}
}