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A Study on Fault Diagnosis Technology of Nuclear Power Plant Based on Decision Tree

[+] Author Affiliations
Yu Mu, Hong Xia

Harbin Engineering University, Harbin, Heilongjiang, China

Paper No. ICONE18-29510, pp. 707-710; 4 pages
doi:10.1115/ICONE18-29510
From:
  • 18th International Conference on Nuclear Engineering
  • 18th International Conference on Nuclear Engineering: Volume 1
  • Xi’an, China, May 17–21, 2010
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 978-0-7918-4929-3
  • Copyright © 2010 by ASME

abstract

The technology of real-time fault diagnosis for NPP has great significance to improve the safety and economy of reactor. At present, expert system, artificial neural network (ANN) and support vector machine (SVM) algorithms are most widely used in the field of NPP fault diagnosis. According to the shortcomings of expert systems, ANN and SVM, the decision tree algorithm is applied in the field of NPP fault diagnosis in this paper. ID3 and C4.5 are applied separately to learn from training samples which are the typical faults of NPP, and diagnose using the acquired knowledge. Then the diagnostic results are compared with the results of SVM method. The results show that: comparing with SVM, decision tree has the advantages of much faster training speed and a little higher accuracy. Furthermore, decision tree can obtain rules from the sample set, so it has good explanatory ability for the diagnostic results.

Copyright © 2010 by ASME

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