An Intelligent Fuzzy Logic Automobile Fault Diagnostic System

Abstract

The advent of intelligent transportation systems and the growing complexity of modern automobiles necessitated innovative approaches to fault detection and diagnostics. This study presents an Intelligent Fuzzy Logic Automobile Fault Diagnostic System. It is designed to enhance the safety and reliability of automotive systems. Fuzzy logic with its ability to handle imprecise and uncertain data is harnessed to develop a robust model capable of identifying and classifying faults in real-time. The system incorporates a hybrid computerized fuzzy system, to aid vehicle owners in identifying issues with their vehicles and providing sound repairs recommendations for any malfunctioning parts. The system was implemented using web technologies; ASP.Net, Bootstrap 3.5, CSS, JavaScript, JQuery and SQL server. The results indicate its potential for widespread adoption, with the ability to reduce accidents and maintenance costs while enhancing the driving experience and provides an accuracy of 73.14% in performance. The Precision 100% and F1 Score 75.72%.