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.
Skip Nav Destination
14th International Conference on Nuclear Engineering
July 17–20, 2006
Miami, Florida, USA
Conference Sponsors:
- Nuclear Engineering Division
ISBN:
0-7918-4242-8
PROCEEDINGS PAPER
Detecting Structural Damage of Nuclear Power Plant by Interactive Data Mining Approach
Yufei Shu
Yufei Shu
RISTEX, Japan Science and Technology Agency, Tokyo, Japan
Search for other works by this author on:
Yufei Shu
RISTEX, Japan Science and Technology Agency, Tokyo, Japan
Paper No:
ICONE14-89697, pp. 261-269; 9 pages
Published Online:
September 17, 2008
Citation
Shu, Y. "Detecting Structural Damage of Nuclear Power Plant by Interactive Data Mining Approach." Proceedings of the 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. pp. 261-269. ASME. https://doi.org/10.1115/ICONE14-89697
Download citation file:
5
Views
0
Citations
Related Proceedings Papers
Related Articles
Developing Data Mining-Based Prognostic Models for CF-18 Aircraft
J. Eng. Gas Turbines Power (October,2011)
A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities
J. Comput. Inf. Sci. Eng (December,2004)
Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data
J. Comput. Inf. Sci. Eng (September,2015)
Related Chapters
Use of PSA in Lisencing of EPR 1600 in Finland (PSAM-0160)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
Privacy-Preserving Outlier Detection over Arbitrarily Partitioned Data
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
Identification of Temporal Interval Relations of Frequent Patterns during Incremental Phase
International Conference on Computer Engineering and Technology, 3rd (ICCET 2011)