Flowgraph Models for Complex Multistate System Reliabiliy.

Conference · Sat Jan 01 00:00:00 EST 2005

OSTI ID:977963

Williams, B J ^[1]; Huzurbazar, A V ^[2]

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1. Brian J.
2. Aparna V.

This chapter reviews flowgraph models for complex multistate systems. The focus is on modeling data from semi-Markov processes and constructing likelihoods when different portions of the system data are censored and incomplete. Semi-Markov models play an important role in the analysis of time to event data. However, in practice, data analysis for semi-Markov processes can be quite difficult and many simplifying assumptions are made. Flowgraph models are multistate models that provide a data analytic method for semi-Markov processes. Flowgraphs are useful for estimating Bayes predictive densities, predictive reliability functions, and predictive hazard functions for waiting times of interest in the presence of censored and incomplete data. This chapter reviews data analysis for flowgraph models and then presents methods for constructing likelihoods when portions of the system data are missing.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• OSTI ID: 977963
• Report Number(s): LA-UR-05-2623; TRN: US201012%%598
• Resource Relation: Conference: Mathematical Models in Reliability 2004 Conference Proceedings, June 2004, Santa Fe, NM
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICAL METHODS AND COMPUTING
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
SYSTEMS ANALYSIS
DATA ANALYSIS
MATHEMATICAL MODELS
RELIABILITY