Nonlinear mixed-effects models for repairable systems reliability

Abstract

Mixed-effects models, also called random-effects models, are a regression type of analysis which enables the analyst to not only describe the trend over time within each subject, but also to describe the variation among different subjects. Nonlinear mixed-effects models provide a powerful and flexible tool for handling the unbalanced count data. In this paper, nonlinear mixed-effects models are used to analyze the failure data from a repairable system with multiple copies. By using this type of models, statistical inferences about the population and all copies can be made when accounting for copy-to-copy variance. Results of fitting nonlinear mixed-effects models to nine failure-data sets show that the nonlinear mixed-effects models provide a useful tool for analyzing the failure data from multi-copy repairable systems.