Bayesian network models are seen as important tools in probabilistic design assessment for complex systems. Such network models for system reliability analysis provide a single probability of failure value whether the experimental data used to model the random variables in the problem are perfectly known or derive from limited experimental data. The values of the probability of failure for each of those two cases are not the same, of course, but the point is that there is no way to derive a Bayesian type of confidence interval from such reliability network models. Bayesian confidence (or belief) intervals for a probability of failure are needed for complex system problems in order to extract information on which random variables are dominant, not just for the expected probability of failure but also for some upper bound, such as for a 95% confidence upper bound. We believe that such confidence bounds on the probability of failure will be needed for certifying turbine engine components and systems based on probabilistic design methods. This paper reports on a proposed use of a two-step Bayesian network modeling strategy that provides a full cumulative distribution function for the probability of failure, conditioned by the experimental evidence for the selected random variables. The example is based on a hypothetical high-cycle fatigue design problem for a transport aircraft engine application.
Skip Nav Destination
e-mail: Jeffrey.Brown@wpafb.af.mil
Article navigation
July 2007
Technical Papers
Confidence Interval Simulation for Systems of Random Variables
Jeffrey M. Brown
e-mail: Jeffrey.Brown@wpafb.af.mil
Jeffrey M. Brown
Structural Modeling Engineer
Air Force Research Laboratory
, Propulsion Directorate, Wright-Patterson AFB, OH 45463
Search for other works by this author on:
Thomas A. Cruse
Chief Technologist (IPA)
Jeffrey M. Brown
Structural Modeling Engineer
Air Force Research Laboratory
, Propulsion Directorate, Wright-Patterson AFB, OH 45463e-mail: Jeffrey.Brown@wpafb.af.mil
J. Eng. Gas Turbines Power. Jul 2007, 129(3): 836-842 (7 pages)
Published Online: October 11, 2005
Article history
Received:
September 16, 2005
Revised:
October 11, 2005
Citation
Cruse, T. A., and Brown, J. M. (October 11, 2005). "Confidence Interval Simulation for Systems of Random Variables." ASME. J. Eng. Gas Turbines Power. July 2007; 129(3): 836–842. https://doi.org/10.1115/1.2718217
Download citation file:
Get Email Alerts
Investigation of Grooved Front Plate for Inlet Swirl Reduction in Brush Seals
J. Eng. Gas Turbines Power
In-Cylinder Imaging and Emissions Measurements of Cold-Start Split Injection Strategies
J. Eng. Gas Turbines Power (August 2025)
Related Articles
A Reliability-Based Approach for Low-Cycle Fatigue Design of Class 2 and 3 Nuclear Piping
J. Pressure Vessel Technol (October,2010)
Updating Performance and Reliability of Concrete Structures Using Discrete Empirical Bayes Methods
J. Offshore Mech. Arct. Eng (November,2002)
Dynamic Reliability Evaluation of Nonrepairable Multistate Weighted k -Out-of- n System With Dependent Components Based on Copula
ASME J. Risk Uncertainty Part B (December,2018)
A Bayesian Approach to Reliability-Based Optimization With Incomplete Information
J. Mech. Des (July,2006)
Related Proceedings Papers
Related Chapters
A PSA Update to Reflect Procedural Changes (PSAM-0217)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
STRUCTURAL RELIABILITY ASSESSMENT OF PIPELINE GIRTH WELDS USING GAUSSIAN PROCESS REGRESSION
Pipeline Integrity Management Under Geohazard Conditions (PIMG)
A Bayesian Approach to Setting Equipment Performance Criteria (PSAM-0438)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)