TABLE OF CONTENTS A.C.M.Fong and S.C.Hui
School of Computer Engineering, Nanyang Technological University,
BlkN4, Nanyang Ave., Singapore 639798
Email: {ascmfong@ntu.edu.sg ; asschui@ntu.edu.sg }
The present trend in vehicle fault diagnosis is toward automation. Modern motor vehicles can often be modeled as a complex system made up of many components, making fault diagnosis difficult. Traditionally, effective vehicle fault diagnosis relies heavily on the experience and knowledge of human experts. This chapter presents the development of an expert system whose aim is to provide useful aid to human users in their attempts at vehicle fault diagnosis, even at remote locations via the WWW. The system employs a hybrid data mining process to effectively mine data stored in a vehicle service database, which contains past service records. Through the learning capability of a neural network, the system is able to generalize knowledge stored in the database. Performance evaluation of the system confirms its effectiveness both in terms of speed and accuracy.
The increasing demand for vehicle fault diagnostic information has led to the development of effective techniques for fault diagnosis. For example, in [Lu et. al., (2000)], the authors describe a fuzzy model that learns automotive diagnostic knowledge using...
