Increasing a knowledge representation schema for FMS control with fault detection and error recovery capabilities

Abstract

This paper presents the integration of Artificial Intelligence representation techniques and high level Petri nets in a knowledge based manufacturing modeling and analysis system. By means of Petri nets and its implementation based on rules, a model of the system can be constructed. In order to verify the real behavior, the control system interchanges signals with the plant. When an abnormal situation is detected, the control system must take control decisions to allow the system to work in the new situation. Error management in these cases includes detection, diagnosis and reaction. In this paper the hierarchy of object classes to control the system is enlarged to face with the problems produced by abnormal behavior.