Paper Info
Title
Fuzzy C-Means Clustering of Signal Functional Principal Components for Post-Processing Dynamic Scenarios of a Nuclear Power Plant Digital Instrumentation and Control System
Abstract
This paper addresses the issue of the classification of accident scenarios generated in a dynamic safety and reliability analyses of a Nuclear Power Plant (NPP) equipped with a Digital Instrumentation and Control system (I&C). More specifically, the classification of the final state reached by the system at the end of an accident scenario is performed by Fuzzy C-Means clustering the Functional Principal Components (FPCs) of selected relevant process variables. The approach allows capturing the characteristics of the process evolution determined by the occurrence, timing, and magnitudes of the fault events. An illustrative case study is considered, regarding the fault scenarios of the digital I&C system of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The results obtained are compared with those of the Kth Nearest Neighbor (KNN), and Classification and Regression Tree (CART) classifiers.
Year
DOI
Venue
2011
10.1109/TR.2011.2134230
IEEE Transactions on Reliability
Keywords
Field
DocType
computerised instrumentation,digital control,fault diagnosis,fuzzy set theory,nuclear engineering computing,nuclear power stations,pattern clustering,power plants,power system reliability,principal component analysis,regression analysis,safety,signal classification,trees (mathematics),CART classifiers,Classification and Regression Tree classifiers,Kth nearest neighbor,Lead Bismuth Eutectic eXperimental Accelerator Driven System,accident scenario classification,control system,dynamic safety analysis,dynamic scenario post-processing,fault event,fault scenario,fuzzy C-means clustering,nuclear power plant digital instrumentation,process evolution,reliability analysis,signal functional principal components,Digital instrumentation and control faults,dynamic reliability,fault scenarios classification,functional principal components analysis,fuzzy C-Means,fuzzy clustering,nuclear power plants
k-nearest neighbors algorithm,Data mining,Fuzzy clustering,Fuzzy logic,Fuzzy set,Control system,Cluster analysis,Digital control,Reliability engineering,Mathematics,Principal component analysis
Journal
Volume
Issue
ISSN
60
2
0018-9529
Citations 
PageRank 
References 
8
0.58
2
Authors
4
Name
Order
Citations
PageRank
Francesco Di Maio1131.70
Piercesare Secchi27011.12
Simone Vantini3609.26
Enrico Zio4777.43