Probabilistic techniques using Monte Carlo sampling for multi- component system diagnostics
- Argonne National Lab., Idaho Falls, ID (United States)
- Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Nuclear Engineering
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.
- Research Organization:
- Argonne National Lab., Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-31-109-ENG-38; FG02-92ER75712
- OSTI ID:
- 90413
- Report Number(s):
- ANL/IFR/CP-85456; CONF-950420-25; ON: DE95012283; TRN: 95:017621
- Resource Relation:
- Conference: International conference on mathematics and computations, reactor physics, and environmental analyses, Portland, OR (United States), 30 Apr - 4 May 1995; Other Information: PBD: [1995]
- Country of Publication:
- United States
- Language:
- English
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