Fault diagnosis decentralized of manufacturing systems using Boolean models

Abstract

This paper introduces an approach decentralized to fault detection and isolation (FDI) in manufacturing systems using a Boolean discrete event model. The method incorporates diverse information sources to create distinct models for plant systems and control. The objective is to enhance the understanding of process operations by employing various representation tools tailored to each information source. It is to reduce the number of explosion problems combinatorial and detect faults in the shortest possible time. This comprehensive representation facilitates the fulfillment of three crucial diagnosis functions: detection, localization, and identification. The approach involves Boolean modeling of each process actuator along with its corresponding sensors, a temporal model based on fuzzy expectations of event occurrences, and a set of if...then rules. The goal of this decentralized approach minimize both the complexity and the manual construction effort required for the model. The paper demonstrates the effectiveness of this approach through an illustrative example involving manufacturing systems.