Skip to main content
Springer Nature Link
Log in

Data Mining for Monitoring Loose Parts in Nuclear Power Plants

  • Conference paper
  • First Online:
Rough Sets and Current Trends in Computing (RSCTC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2005))

Included in the following conference series:

Abstract

Monitoring the mechanical impact of a loose (detached or drifting) part in the reactor coolant system of a nuclear power plant is one of the essential functions for operation and maintenance of the plant.Large data tables are generated during this monitoring process. This data can be “mined ” to reveal latent patterns of interest to operation and maintenance. Rough set theory has been applied successfully to data mining. It can be used in the nuclear power industry and elsewhere to identify classes in datasets, finding dependencies in relations and discovering rules which are hidden in databases. This paper can be considered as one of a series, the earlier ones being summarized in Guan & Bell (2000a). These methods can be used to understand and control aspects of the causes and effects of loose parts in nuclear power plants. So in this paper we illustrate the use of our data mining methods by means of a running example using Envelope Rising Time data ERT on monitoring loose parts in nuclear power plants.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: a performance perspective, IEEE Transactions on Knowledge and Data Engineering (Special issue on Learning and Discovery in Knowledge-Based Databases), 5(6), 914–925.

    Google Scholar 

  2. Grzymala-Busse, J. (1991). Managing uncertainty in expert systems. Kluwer Academic Publishers.

    Google Scholar 

  3. Guan, J.W., & Bell, D.A. (1991). Evidence theory and its applications,Vol.1 (Studies in Computer Science and Artificial Intelligence 7). Elsevier.

    Google Scholar 

  4. Guan, J.W., & Bell, D.A. (2000a). Rough knowledge discovery for nuclear safety, International Journal of General Systems, Vol.29(2000), No.2, 231–249.

    Article  MATH  Google Scholar 

  5. Guan, J.W., & Bell, D.A. (2000b). Knowledge discovery for controlling nuclear power plants, Proc. ofthe 4th International FLINS Conference on Intelligent Techniques and Soft Computing in Nuclear Science and Engineering (World Scientific, Singapore), August 28-30, 2000, Bruges, Belgium.( http://www.sckcen.be/.ins2000)

  6. Oh, Y. G. et al (1996). Fuzzy logic utilization for the diagnosis of metallic loose part impact in nulear power plant, Intelligent System and Soft Computing for Nuclear Science and Industry, Proceedings of the 2nd International FLINS Workshop, Mol, Belgium, September 25-27, 1996, 372–378.

    Google Scholar 

  7. Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning about data. Kluwer.

    Google Scholar 

  8. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. CACM, 38(11), 89–95.

    Google Scholar 

  9. Polkowski, L.; Skowron, A. 1998a, Rough sets in knowledge discovery, Vol.1, Methodology and Applications, Physica-Verlag, A Springer-Verlag Company, 1998.

    Google Scholar 

  10. Polkowski, L.; Skowron, A. 1998b, Rough sets in knowledge discovery, Vol.2, Applications, Case Studies, and Software Systems, Physica-Verlag, A Springer-Verlag Company, 1998.

    Google Scholar 

  11. Yao, Y.Y., & Wong S. K.M.( 1992). A decision theoretic framework for approximating concepts. International Journal of Man-machine Studies, 37, 793–809.

    Article  Google Scholar 

  12. Yao, Y.Y., & Lin T. Y.( 1996).General ization of rough sets using modal logics, Intelligent automation and soft computing, Vol.2(1996), No.2, 103–120.

    Google Scholar 

  13. Ziarko, W. (1991). The discovery, analysis, and representation of data dependencies in databases. In G. Piatetsky-Shapiro, & W.J. Frawley (Eds.), Knowledge Discovery in Databases (pp.177–195). AAAI Press/MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Software Engineering, University of Ulster at Jordanstown, BT37 0QB, Northern Ireland, UK

    J. W. Guan & D. A. Bell

Authors
  1. J. W. Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. A. Bell
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina Regina, S4S 0A2, Saskatchewan, Canada

    Wojciech Ziarko  & Yiyu Yao  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guan, J.W., Bell, D.A. (2001). Data Mining for Monitoring Loose Parts in Nuclear Power Plants. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_38

Download citation

  • DOI: https://doi.org/10.1007/3-540-45554-X_38

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43074-2

  • Online ISBN: 978-3-540-45554-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics