A Class of Models for Degradation Data with Dynamic Covariates
Degradation data provide a useful resource for obtaining reliability information for products and systems with high reliability. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage and as well as other environmental variables such as temperature and humidity, which we refer to as dynamic covariate information. In this paper, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use general path models with individual random effects to describe degradation paths and parametric models to describe the covariate process. Physically motivated models and regression splines are proposed to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing the estimate of the lifetime distribution induced by the proposed degradation path model.
Covariate process Environmental conditions Lifetime prediction Shape restricted splines System health monitoring Usage history
YILI HONG WILLIAM Q. MEEKER
Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA Department of Statistics, Iowa State University, Ames, IA 50011, USA
国际会议
北京
英文
421-427
2011-06-20（万方平台首次上网日期，不代表论文的发表时间）