Probabilistic techniques using Monte Carlo sampling for multi- component system diagnostics

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Description

We outline the structure of a new approach at multi-component system fault diagnostics which utilizes detailed system simulation models, uncertain system observation data, statistical knowledge of system parameters, expert opinion, and component reliability data in an effort to identify incipient component performance degradations of arbitrary number and magnitude. The technique involves the use of multiple adaptive Kalman filters for fault estimation, the results of which are screened using standard hypothesis testing procedures to define a set of component events that could have transpired. Latin Hypercube sample each of these feasible component events in terms of uncertain component reliability data and ... continued below

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12 p.

Creation Information

Aumeier, S.E.; Lee, J.C. & Akcasu, A.Z. June 1, 1995.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Aumeier, S.E. Argonne National Lab., Idaho Falls, ID (United States)
  • Lee, J.C.
  • Akcasu, A.Z. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Nuclear Engineering

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  • Argonne National Laboratory
    Publisher Info: Argonne National Lab., Idaho Falls, ID (United States)
    Place of Publication: Idaho Falls, Idaho

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Description

We outline the structure of a new approach at multi-component system fault diagnostics which utilizes detailed system simulation models, uncertain system observation data, statistical knowledge of system parameters, expert opinion, and component reliability data in an effort to identify incipient component performance degradations of arbitrary number and magnitude. The technique involves the use of multiple adaptive Kalman filters for fault estimation, the results of which are screened using standard hypothesis testing procedures to define a set of component events that could have transpired. Latin Hypercube sample each of these feasible component events in terms of uncertain component reliability data and filter estimates. The capabilities of the procedure are demonstrated through the analysis of a simulated small magnitude binary component fault in a boiling water reactor balance of plant. The results show that the procedure has the potential to be a very effective tool for incipient component fault diagnosis.

Physical Description

12 p.

Notes

INIS; OSTI as DE95012283

Source

  • International conference on mathematics and computations, reactor physics, and environmental analyses, Portland, OR (United States), 30 Apr - 4 May 1995

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  • Other: DE95012283
  • Report No.: ANL/IFR/CP--85456
  • Report No.: CONF-950420--25
  • Grant Number: W-31-109-ENG-38;FG02-92ER75712
  • Office of Scientific & Technical Information Report Number: 90413
  • Archival Resource Key: ark:/67531/metadc794432

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  • June 1, 1995

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  • Dec. 19, 2015, 7:14 p.m.

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  • Jan. 14, 2016, 6:56 p.m.

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Aumeier, S.E.; Lee, J.C. & Akcasu, A.Z. Probabilistic techniques using Monte Carlo sampling for multi- component system diagnostics, article, June 1, 1995; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc794432/: accessed September 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.