A fuzzy-based genetic approach to the diagnosis of manufacturing systems

Abstract This paper describes the development of a hybrid approach that integrates graph theory, fuzzy sets and genetic algorithms for the diagnosis of manufacturing systems. The approach enables the modelling of causal relations of system components in manufacturing systems. Based on the model thus established, a worst-first search technique has been proposed and developed for the identification of probable fault-propagation paths. As manufacturing diagnosis often involves the interpretation of uncertainty, fuzzy-set theory is employed for this purpose. Unlike conventional diagnostic systems which assume that all the system components or nodes of a manufacturing system model are measurable, the genetic-algorithm-based search engine developed in this work is able to deal with nodes that cannot be, or are not, measured. Details of the hybrid approach, the worst-first search technique and the genetic-algorithms-based search engine are discussed. The framework of a prototype fuzzy-based genetic diagnostic system is described. Details of the system validation are also presented.