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Detecting Structural Damage of Nuclear Power Plant by Interactive Data Mining Approach

[+] Author Affiliations
Yufei Shu

RISTEX, Japan Science and Technology Agency, Tokyo, Japan

Paper No. ICONE14-89697, pp. 261-269; 9 pages
  • 14th International Conference on Nuclear Engineering
  • Volume 1: Plant Operations, Maintenance and Life Cycle; Component Reliability and Materials Issues; Codes, Standards, Licensing and Regulatory Issues; Fuel Cycle and High Level Waste Management
  • Miami, Florida, USA, July 17–20, 2006
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 0-7918-4242-8 | eISBN: 0-7918-3783-1
  • Copyright © 2006 by ASME


This paper presents a nonlinear structural damage identification technique, based on an interactive data mining approach, which integrates a human cognitive model in a data mining loop. A mining control agent emulating human analysts is developed, which directly interacts with the data miner, analyzing and verifying the output of the data miner and controlling the data mining process. Additionally, an artificial neural network method, which is adopted as a core component of the proposed interactive data mining method, is evolved by adding a novelty detecting and retraining function for handling complicated nuclear power plant quake-proof data. Plant quake-proof testing data has been applied to the system to show the validation of the proposed method.

Copyright © 2006 by ASME



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