A fuzzy-based genetic approach to the diagnosis of manufacturing systems |
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| Affiliation: | 1. School of Mechanical and Production Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798;2. Gintic Institute of Manufacturing Technology, Nanyang Technological University, Singapore 639798;3. Hewlett Packard Singapore Private Limited, 20, Gulway, 629196, Singapore;1. Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur 713206, India;2. Department of Mathematics, National Institute of Technology, Durgapur 713209, India;1. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China;2. School of Economics, Dongbei University of Finance and Economics, Dalian, 116025, China;3. School of Urban and Regional Science, East China Normal University, Shanghai, 200062, China;4. School of Architecture and Urban Planning, Guangdong University of Technology, 510090, Guangzhou, China;1. CEMISID, Universidad de Los Andes, Mérida, Venezuela;2. GIDTEC-Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca, Ecuador;3. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;4. Universidade do Algrave, Faro, Portugal;1. Department of Anaesthesia, Pain Management and Perioperative Medicine, Halifax, NS, Canada;2. Department of Pathology, Dalhouse University, Halifax, NS, Canada;3. Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada |
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| 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. |
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