A fuzzy decision tree for fault classification.

Full Abstract

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

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Author information

Author/s: Zio, Enrico (E); Baraldi, Piero (P); Popescu, Irina C (IC);

Affiliation: Enegery Department, Polytechnic of Milan, Milan, Italy. enrico.zio(-atsign-)polimi.it

Journal and publication information

Publication Type: Journal Article

Journal: Risk analysis : an official publication of the Society for Risk Analysis (Risk Anal), published in United States. (Language: eng)

Reference: 2008-Feb; vol 28 (issue 1) : pp 49-67

Dates: Created 2008/02/28; Completed 2008/08/06;

PMID: 18304106, status: MEDLINE (last retrieval date: 11/6/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Accidents, Occupational - prevention & control
Algorithms
Cluster Analysis
Decision Making, Computer-Assisted
Decision Trees

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