Bayesian Estimation of Reliability for Batches of High Reliability Single-Use Parts

ABSTRACT

When batches of critical, very high-reliability single-use parts are being produced, rigorous testing is often required to qualify the parts and allow them to be used by the customer. Frequentist and Bayesian approaches are described for predicting the reliability of the remaining subset of the batch, conditional on all of the other tested parts working correctly. Answers from different methods are compared, their strengths and weaknesses are considered, and their robustness to initial assumptions are examined. Some related questions are explored to consider the impact on reliability from different choices of the relative number of the tested and sale units, and the condition for passing the batch from both the manufacturer's and customers’ points of view. We describe the approach in the context of automotive air bag inflation devices on most vehicles, but the approach is relevant to batches of single-use parts that have a very high requirement for reliability and must be destructively tested.

KEYWORDS:

• batch testing
• destructive testing
• incorporating expert knowledge
• very high reliability