Bayesian Nonparametric Models for Combining Heterogeneous Reliability Data

• Activity Number: 441
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Richard L. Warr*+ and David H Collins
• Companies: Air Force Institute of Technology and Los Alamos National Laboratory
• Keywords: Dirichlet Process ; Hierarchical Modeling ; Lifetime Prediction ; Parallel and Series Systems

Abstract

Modern complex engineering systems often present the analyst with a mix of data types that can be used for reliability prediction: system test results, lifetime data from unit tests of components, and subsystems data, all of which may have predictive value for the system lifetime. We present a hierarchical nonparametric framework, using Dirichlet processes, in which time-to-event distributions may be estimated from sample data or derived based on physical failure mechanisms. By applying a Bayesian methodology, the framework can incorporate prior information, including expert opinion.