Fault diagnosis of a cstr using fuzzy neural networks |
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| Affiliation: | 1. National Industry-Education Platform for Energy Storage, Tianjin University, Tianjin, 300350, China;2. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, China;3. Power Dispatching and Control Center, Inner Mongolia Power (Group) Co., Ltd, Hohhot, 010020, China;1. Science and Technology on Liquid Rocket Engine Laboratory, Xi''an Aerospace Propulsion Institute, Xi''an 710100, PR. China;2. School of Aeronautics, Northwestern Polytechnical University, Xi''an 710100, PR. China;3. Jiangsu Key Laboratory of Process Enhancement and Energy Equipment Technology, School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, PR. China;4. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, PR. China;1. Department of Chemical and Biological Engineering, Koç University, Istanbul 34450, Turkey;2. Koç University TUPRAS Energy Center (KUTEM), Koç University, Istanbul, 34450, Turkey |
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| Abstract: | On-line process fault diagnosis using fuzzy neural networks is described in this paper. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feed forward neural network. The fuzzification layer converts increments in on-line measurements and controller outputs into three fuzzy sets: “increase”, “steady”, and “decrease”. Abnormalities in a process are represented by qualitative increments in on-line measurements and controller outputs. These are classified into various categories by the network. By representing abnormalities in qualitative form, training data can be condensed. The fuzzy approach ensures smooth transitions from one fuzzy sets to another and, hence, robustness to measurement noise is enhanced. The technique has been successfully applied to a CSTR system. |
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