A General Framework for Learning Curve Reliability Growth Models

In reliability growth models, system performance improves during prototype testing, as design changes are made and operating procedures and the environment are modified. There is great interest in predicting the ultimate performance of the system, using only the epochs of the failures that occur early in the testing program. This paper constructs a general framework for learning-curve models of reliability growth, including many different model variations that have previously been analyzed. Numerical trials indicate the difficulty of estimating ultimate performance; the maximum likelihood estimator is unstable for small testing intervals with a small number of systems on test. Bayesian procedures are recommended for implementation.