Abstract:
The vehicle is a complex system which requires a variety of parts for appropriate
functioning. Because of this complexity there are numerous things which can
malfunction in a vehicle. Diagnosing malfunctioning parts and fixing those issues in
vehicles are very complex and time consuming due to the varying technologies used
in vehicles. According to literature, since 1980s, expert systems have been the ideal
technology for the diagnosing task in physical and biological systems. However, the
use of expert systems in Sri Lanka is very limited. This project presents the
development of an expert system to diagnose problems in vehicles. The research
mainly focused on mechanical problems which might occur in vehicles. There are
variety of users that might find the system beneficial. Users of the developed system
include expert mechanics, novice mechanics, drivers and vehicle owners. Managers of
garages can also be benefited by this system when doing preliminary investigations.
Requirements that were needed to develop the expert system were gathered by
interviewing the staff of the project sponsor. Some requirements were taken through
manuals and from the Internet. Expert system has been developed by using the client
server architecture. Apache Tomcat was used as the web server. To develop the expert
system e2gRuleEngine shell had been used. The shell contains the knowledge base as
well as the inference engine. The rules requiring for inference making are stored in
the knowledge base. New rules for the knowledge base could be added by using the
e2gRuleWriter. Inference engine uses forward chaining technique to infer. According
to the selection by the user rules were matched and matching rules were fired and a
solution was given. Working with incomplete information, provision of
reasons/explanations for answers and uncertainty handling are also built into the
inference engine. The developed expert system was first evaluated by the expert
mechanics. By expert evaluation it could be tested whether the correct rules have been
added to the system. A questionnaire was designed and it was used to check the userfriendliness and the usability of the developed expert system. A questionnaire was
distributed among a sample which has taken from simple random sampling.
According to the statistical analysis it can be concluded that expert system works on
par with human expert with the 84% confidence.
