A knowledge-based diagnosis system for automobile engines

Abstract

A newly-developed knowledge-based diagnosis system for automobile engines is described in this paper. The system is based on the Hierarchical Diagnosis Principle, suggested by the authors. According to this principle, a complex diagnostic task can be divided into several simple ones and then solved step-by-step. Both deep and shallow knowledge are used in the system, and organised in two different knowledge bases:

• ⊙ A static knowledge base, which uses frames to describe the structure, symptom and fault information of the system to be diagnosed;

• ⊙ A dynamic knowledge base, which uses production rules and special functions to describe various dynamic information for diagnosing the locations and causes of a system fault.

The system employs a hierarchical and modular architecture which has two levels: a meta-level and an object-level. The knowledge base of the object-level system, according to the fault types and structure hierarchy of the system to be diagnosed, is divided into several independent knowledge sources which are controlled by the meta-level system. The knowledge sources communicate with each other through a working memory called a ‘blackboard’.