Authors :
Maureen Akazue; John Ashie; Abel Edje
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/5bj5am3n
Scribd :
https://tinyurl.com/3ruk5jn5
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB1293
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%.
Keywords :
Fuzzy Logic, Fault Detection, Knowledge-based, Fuzzification and Defuzzification.
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%.
Keywords :
Fuzzy Logic, Fault Detection, Knowledge-based, Fuzzification and Defuzzification.
