Practical reliability and uncertainty quantification in complex systems : final report.

Grace, Matthew D. ; Ringland, James T. ; Marzouk, Youssef M. (Massachusetts Institute of Technology, Cambridge, MA) ; Boggs, Paul T. ; Zurn, Rena M. ; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM) ; Pebay, Philippe Pierre ; Red-Horse, John

关键词: DATA COVARIANCES;    SYSTEMS ANALYSIS;    MONTE CARLO METHOD;    POLYNOMIALS;    RELIABILITY;    TESTING;    TIME DEPENDENCE;    PROBABILITY DENSITY FUNCTIONS;   

DOI  :  10.2172/993614
RP-ID  :  SAND2009-6072
PID  :  OSTI ID: 993614
Others  :  TRN: US201024%%14

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The purpose of this project was to investigate the use of Bayesian methods for the estimation of the reliability of complex systems. The goals were to find methods for dealing with continuous data, rather than simple pass/fail data; to avoid assumptions of specific probability distributions, especially Gaussian, or normal, distributions; to compute not only an estimate of the reliability of the system, but also a measure of the confidence in that estimate; to develop procedures to address time-dependent or aging aspects in such systems, and to use these models and results to derive optimal testing strategies. The system is assumed to be a system of systems, i.e., a system with discrete components that are themselves systems. Furthermore, the system is 'engineered' in the sense that each node is designed to do something and that we have a mathematical description of that process. In the time-dependent case, the assumption is that we have a general, nonlinear, time-dependent function describing the process. The major results of the project are described in this report. In summary, we developed a sophisticated mathematical framework based on modern probability theory and Bayesian analysis. This framework encompasses all aspects of epistemic uncertainty and easily incorporates steady-state and time-dependent systems. Based on Markov chain, Monte Carlo methods, we devised a computational strategy for general probability density estimation in the steady-state case. This enabled us to compute a distribution of the reliability from which many questions, including confidence, could be addressed. We then extended this to the time domain and implemented procedures to estimate the reliability over time, including the use of the method to predict the reliability at a future time. Finally, we used certain aspects of Bayesian decision analysis to create a novel method for determining an optimal testing strategy, e.g., we can estimate the 'best' location to take the next test to minimize the risk of making a wrong decision about the fitness of a system. We conclude this report by proposing additional fruitful areas of research.