Classification of faults in Nuclear Power Plant

M. Awadalla, A. K. Abdien, S. M. Rashad, A. Ahmed, D. Al Abri

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, the performance of traditional Support Vector Machine (SVM) is improved using Genetic Algorithm (GA). GA is used to determine the optimal values of SVM parameters that assure highest predictive accuracy and generalization ability simultaneously. The proposed scheme, called Support Vector Machine Genetic Algorithm (SVM-GA) Scheme, is applied on a beforehand data of a Nuclear Power Plant (NPP) to classify its associated faults. Compared to the standard SVM model, simulation of SVM-GA indicates its superiority when applied on the dataset with unbalanced classes. SVM-GA scheme can gain higher classification with accurate and faster learning speed.

Original languageEnglish
Pages (from-to)274-284
Number of pages11
JournalWSEAS Transactions on Systems
Volume13
Publication statusPublished - 2014

Fingerprint

Nuclear power plants
Support vector machines
Genetic algorithms

Keywords

  • Fault classification
  • Genetic Algorithm (GA)
  • Machine learning
  • Multi fault classification
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering

Cite this

Classification of faults in Nuclear Power Plant. / Awadalla, M.; Abdien, A. K.; Rashad, S. M.; Ahmed, A.; Al Abri, D.

In: WSEAS Transactions on Systems, Vol. 13, 2014, p. 274-284.

Research output: Contribution to journalArticle

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