Nonparametric Bayesian Analysis for Masked Data From Hybrid Systems in Accelerated Lifetime Tests

Abstract

Under constant stress accelerated test, we analyze the masked data from hybrid systems including series–parallel system and parallel–series system. The Bayesian posterior distribution and estimates of components’ subsurvival functions are obtained by assuming a prior of the multivariate Dirichlet process. By establishing a relationship between subsurvival and survival functions of components, the estimates of reliabilities for components and system are derived from the estimates of subsurvival functions. It is not confined to the common restriction that the sets of discontinuity points of the survival functions have to be disjointed. For a complex system, we represent it to the proposed series–parallel system or parallel–series system. Thus, the nonparametric Bayesian approach is also applicable for the complex system with s-independent components. A simulated example is presented to demonstrate the efficiency of the method. Finally, the method is applied to a real data of coal wine monitoring power.