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Identification of faults in a level control dynamic system

  • Francesco Di Maio
  • , Marco Stasi
  • , Enrico Zio
  • , Diego Mandelli
  • , Tunc Aldemir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Identification of faults is an important task in system safety analysis. The large number of system scenarios considered in a realistic safety assessment need to be post-processed to identify the system critical states. The task can be quite complex when the dynamic aspects of the system behavior and its control play a relevant role in the analysis. For example, a fault event may lead to different scenario evolutions depending on the time of failure occurrence and the corresponding state of the controlled process variables. In this work, a Fuzzy C-Means clustering algorithm is applied for the classification of the fault scenarios of a level control system. The classification is based on information from both the stochastic events sequence and the patterns of the process variables evolution.

Original languageEnglish
Title of host publication6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2009
Pages1228-1239
Number of pages12
StatePublished - 2009
Event6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2009 - Knoxville, TN, United States
Duration: Apr 5 2009Apr 9 2009

Publication series

Name6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2009
Volume2

Conference

Conference6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2009
Country/TerritoryUnited States
CityKnoxville, TN
Period04/5/0904/9/09

Keywords

  • Dynamic reliability
  • Fault scenarios classification
  • Fuzzy C-means
  • Fuzzy clustering
  • Level control system

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