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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Qin, Zhang., Xuegao, An., Jin, Gu., Bingquan, Zhao., Dazhi, Xu., Shuren, Xi.: Application of FBOLES—Prototype Expert System for Fault Diagnosis in Nuclear Power Plants. Reliability Engineering and System Safety, Vol. 44. (1994) 225–235
Uhrig, Robert E., Tsoukalas, Lefteri H.: Soft Computing Technologies Nuclear Engineering Applications. Process in Nuclear Energy, Vol. 34(1). (1999) 13–75
HOLLAND, J. H.: Genetic Algorithms. Scientific America, Vol. 267. (1992) 66–72
K, F, Man., K, S, Tang., S, Kwong.: Genetic algorithms: concepts and designs. Springer, London New York (1999)
Dipankar, D.: Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms. Applied Intelligence, Vol. 8. (1998) 113–121
Peter, K. S., Robin, P. G.: Efficient GA Based Techniques for Classification. Applied Intelligence, Vol. 11. (1999) 277–284
Fushuan, Wen., Zhenxiang, Han.: Fault section estimation in power systems using genetic algorithm. Electric Power Systems Research, Vol. 34(3). (1995) 165–172
Lee, H. M., Sheu, C. C., Chen, J. M.: Handwritten Chinese character recognition based on primitive and fuzzy features via the SEART neural net model. Applied Intelligence, Vol. 8. (1998) 269–285
Yi, L., Tie, Q. C.: A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis. Applied Intelligence, Vol. 9. (1998) 231–243
Dilip, K. P., Kalyanmoy, D., Amitabha, G., A genetic-fuzzy approach for mobile robot navigation among moving obstacles. International Journal of Approximate Reasoning, Vol. 20. (1999)
Wael A. Farag., Victor H. Quintana., Germano Lambert-Torres.: A Genetic-Based Neuro-Fuzzy Approach for Modeling and Control of Dynamical Systems. IEEE Transaction on Neural Network, Vol. 9(5). (1998) 756–767
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-45517-5_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42219-8
Online ISBN: 978-3-540-45517-2
eBook Packages: Springer Book Archive