Salama A. Mostafa1, Mohd Sharifuddin Ahmad1, Mazin Abed Mohammed1 and Omar Ibrahim Obaid1
Abstract
Applications in fault diagnosis are continuously being
implemented to serve different sectors. Car failure detection is a
sequence of diagnostic processes that necessitates the deployment
of expertise. The Expert System (ES) is one of the leading
Artificial Intelligence (AI) techniques that have been adopted to
handle such task. This paper presents the imperatives for an ES in
developing car failure detection model and the requirements of
constructing successful Knowledge-Based Systems (KBS) for
such model. In addition, it exhibits the adaptation of the ES in the
development of Car Failure and Malfunction Diagnosis
Assistance System (CFMDAS). However, CFMDAS
development faces many challenges such as collecting the
required data for building the knowledge base and performing the
inferencing. Furthermore, diagnosis of car faults requires high
technical skills and experienced mechanics who are typically
scarce and expensive to get. Thus, systems such as CFMDAS can
be highly useful in assisting mechanics for failure detection and
training purposes. Moreover, capturing and retaining valuable
knowledge on such domain yield more accurate and less time
consuming models.
Publication Date
8/3/2012
Publisher
IJCSI International Journal of Computer Science Issues,
Keywords
Expert system (ES), Artificial Intelligence (AI), car
fault, Knowledge-Based System (KBS), Inferenc
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