By Topic

Enhanced Neural Network Based Fault Detection of a VVER Nuclear Power Plant With the Aid of Principal Component Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hadad, K. ; Aerosp. & Mech. Eng. Dept., Univ. of Arizona, Tucson, AZ ; Mortazavi, M. ; Mastali, M. ; Safavi, A.A.

This paper presents a neural network based fault diagnosing approach which allows dynamic fault identification. The method utilizes the principal component analysis (PCA) technique to dramatically reduce the problem dimension. Such a dimension reduction approach leads to faster diagnosing and allows a better graphical presentation of the results. To show the effectiveness of the proposed approach, two methodologies are used to train the neural network (NN). At first, a training matrix composed of 15 variables is used to train a multilayer perceptron neural network (MLP) with resilient backpropagation (RP) algorithm. Employing the proposed method, a more accurate and simpler network is designed where the input size is reduced from 15 to 6 variables for training the NN. In short, the application of PCA highly reduces the network topology and allows employing more efficient training algorithms. The developed networks use, as input, a short set (in a moving temporal window (MTW)) of recent measurements of each variable avoiding the necessity of using starting events. The accuracy, generalization ability and reliability of the designed networks are verified using 10 simulated events data from a VVER-1000 simulator. Noise is added to the data to evaluate robustness of the method, and the method again shows to be effective and powerful.

Published in:

Nuclear Science, IEEE Transactions on  (Volume:55 ,  Issue: 6 )