Statistical Methods for Degradation Data with Dynamic Covariates and an Application to Outdoor Weathering Prediction

• Activity Number: 441
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): William Q. Meeker*+ and Yili Hong
• Companies: Iowa State University and Virginia Tech
• Keywords: Covariate process ; Environmental conditions ; Lifetime prediction ; Organic coatings ; System health monitoring ; Usage history

Abstract

Degradation data provid reliability information for high reliability products and systems. 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 load, 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 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. The proposed methods are illustrated with an application for predicting the life of organic paints and coatings in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity).