A Graphical Technique and Penalized Likelihood Method for Identifying and Estimating Infant Failures

Abstract

Field failure data often exhibit extra heterogeneity as early failure data may have quite different distribution characteristics from later failure data. These infant failures may come from a defective subpopulation instead of the normal product population. Many exiting methods for field failure analyses focus only on the estimation for a hypothesized mixture model, while the model identification is ignored. This paper aims to develop efficient, accurate methods for both detecting data heterogeneity, and estimating mixture model parameters. Mixture distribution detection is achieved by applying a mixture detection plot (MDP) on field failure observations. The penalized likelihood method, and the expectation-maximization (EM) algorithm are then used for estimating the components in the mixture model. Two field datasets are employed to demonstrate and validate the proposed approach.