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Fault diagnostics and evaluation in cryogenic loading system using optimization algorithm

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
  • Ekaterina Ponizovskaya-Devine
  • Dmitry G. Luchinsky
  • Anu Kodali
  • Michael Khasin
  • Dogan Timucin
  • Jarred Sass
  • Jose Perotti
  • Barbara Brown
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Publication date1/01/2015
Host publicationPHM 2015 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015
EditorsMatthew J. Daigle, Anibal Bregon
PublisherPrognostics and Health Management Society 2015
Pages21-29
Number of pages9
Volume6
ISBN (electronic)9781936263202
<mark>Original language</mark>English
Event2015 Annual Conference of the Prognostics and Health Management Society, PHM 2015 - San Diego, United States
Duration: 18/10/201522/10/2015

Conference

Conference2015 Annual Conference of the Prognostics and Health Management Society, PHM 2015
Country/TerritoryUnited States
CitySan Diego
Period18/10/1522/10/15

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
ISSN (Print)2325-0178

Conference

Conference2015 Annual Conference of the Prognostics and Health Management Society, PHM 2015
Country/TerritoryUnited States
CitySan Diego
Period18/10/1522/10/15

Abstract

Physics-based approach to the cryogenic flow health management is presented. It is based on fast and time-accurate physics models of the cryogenic flow in the transfer line. We discuss main features of one of these models - the homogeneous moving front model - and presents results of its validation. The main steps of the approach including fault detection, identification, and evaluation are discussed. A few examples of faults are presented. It is shown that dynamic features of the faults naturally form a number of ambiguity groups. A D-matrix approach to optimized identification of these faults is briefly outlined. An example of discerning and evaluating faults within one ambiguity group using optimization algorithm is considered in more details. An application of this approach to the Integrated Health Management of cryogenic loading is discussed.