Bayesian Hierarchical Models for Common Components Across Multiple System Configurations

• Activity Number: 370
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Kassie Fronczyk* and Rebecca Dickinson and Alyson Wilson and Caleb Browning and Laura Freeman
• Companies: Institute for Defense Analyses and Institute for Defense Analyses and North Carolina State University and North Carolina State University and IDA
• Keywords: Combining Information ; Defense Acquisition ; Reliability ; Reliability Growth

Abstract

Reliability is a high priority in the testing of military systems. A common interest is reliability growth, which tracks the change in the reliability of a system as it moves through test phases and undergoes periodic corrective actions. A Bayesian hierarchical model is used to assess the reliability of the Joint Light Tactical Vehicle (JLTV), a family of vehicles with many commonalities designed to replace one-third of the legacy Humvee fleet. The JLTV has been through a series of three developmental test events, where fixes occurred only during a set corrective action period. The proposed model effectively combines information across test phase and common vehicle components, data not typically leveraged by the Department of Defense (DoD) test community. Inferential objectives include the mean miles between failure for each vehicle, failure mode, and phase of test. Also of interest is the fixed effectiveness factor, a measure of how well repairs improve failure rates. Additionally, this data-driven approach improves on current growth models used in the DoD that are based on fixed quantities set by management.