A time-varying copula-based prognostics method for bivariate accelerated degradation testing

Accelerated degradation testing (ADT) has been widely used to accelerate failure/degradation processes and to quickly evaluate the reliability and lifetime of products. In particular, the application of copula function provides a convenient and efficient way to model the ADT data of products that have two or more s-dependent degradation measures. However, little effort has focused on the pointwise infimum and supremum of the multivariate joint-distribution function. For this paper, a novel prognostics method was developed for bivariate ADT data on the basis of Brownian motion and time-varying copula method, which can estimate the pointwise best-possible bounds on bivariate joint reliable life function with a given measure of association, such as Kendall's tau or Spearman's rho. The proposed model is applied to the real ADT data of microwave assembly to illustrate its performance and effectiveness.