Abstract
There is an increasing trend toward introducing artificial intelligence into the fault diagnosis of nuclear power plants. However, processing imperfect information and uncertainty is the art of the fault diagnosis. This paper describes a fault diagnosis method based on genetic algorithms and fuzzy logic. This method utilizes the strings in genetic algorithms to simulate the various possible assemblies of results and updates the results with the evaluation. A new evalua- tion method in genetic algorithms is adopted. When calculating the fitness of strings, fuzzy logic is used to process the multi-knowledge: expert knowledge, mini-knowledge tree model and standard signals. Experiments on simulator show the advantages of this method in processing illusive and real-time signals, imperfect diagnosis knowledge and other instances.
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Zhou, Y., Fang, X., Zhao, B. (2001). Diagnosis Based on Genetic Algorithms and Fuzzy Logic in NPPs. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_40
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DOI: https://doi.org/10.1007/3-540-45517-5_40
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