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Abstract:
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The reliability of a weapon system is an essential component of its suitability for operational deployment. Yet, in an era of reduced budgets and limited testing, verifying that reliability requirements have been met can be challenging, particularly using traditional analysis methods that depend on a single set of data coming from a single test phase. In this presentation, we highlight the value of using Bayesian approaches to assess the reliability for several Department of Defense vehicle programs. In particular, we are able to effectively combine information across test phases and vehicles. Additionally, we show how the information learned through previous phases of test can be leveraged to plan future test events.
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