Title :
Selecting features for nuclear transients classification by means of genetic algorithms
Author :
Zio, E. ; Baraldi, P. ; Pedroni, N.
Author_Institution :
Dept. of Nucl. Eng., Politecnico di Milano, Milan, Italy
fDate :
6/1/2006 12:00:00 AM
Abstract :
The issue of feature selection is particularly critical for the application of monitoring and "on condition" diagnostic techniques to complex plants, like the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features unnecessarily increase the complexity of the problem and can degrade the diagnostic performance. In this paper, the problem of choosing among the several measured plant parameters those to be used for efficient, early transient diagnosis is tackled by means of genetic algorithms. Three different schemes for simultaneously performing the feature selection and the training of an empirical diagnostic classifier are presented. The first approach employs a single objective genetic algorithm to search the vector of features optimal with respect to the classification performance of a Fuzzy K-Nearest Neighbors classifier. With reference to the same classifier, the second and third approaches embrace a multi-objective perspective to find feature sets that achieve high classification performances with low numbers of features. In all cases, validation of the performance of the classifiers based on the optimal feature subsets identified by the genetic algorithm is successively carried out with respect to transients never used during the feature selection phase. The effectiveness of the proposed approaches is tested on a diagnostic problem regarding the classification of simulated transients in the feedwater system of a Boiling Water Reactor.
Keywords :
fission reactor monitoring; fission reactor operation; genetic algorithms; nuclear engineering computing; nuclear power stations; Boiling Water Reactor; Fuzzy K-Nearest Neighbors classifier; diagnostic techniques; fault diagnosis; feedwater system; nuclear power plants; nuclear transients classification; plant parameters; single objective genetic algorithm; Condition monitoring; Degradation; Fault diagnosis; Genetic algorithms; Inductors; Nuclear measurements; Particle measurements; Pattern recognition; Power generation; Power measurement; Boiling water reactor; Fuzzy K-Nearest Neighbors; fault diagnosis; feature selection; feedwater system; genetic algorithms;
Journal_Title :
Nuclear Science, IEEE Transactions on
DOI :
10.1109/TNS.2006.873868