Accelerated Degradation Tests Planning with Competing Failure Modes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

21 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)142-155
Journal / PublicationIEEE Transactions on Reliability
Volume67
Issue number1
Online published31 Oct 2017
Publication statusPublished - Mar 2018

Abstract

Accelerated degradation tests (ADT) have been widely used to assess the reliability of products with long lifetime. For many products, environmental stress not only accelerates their degradation rate but also elevates the probability of traumatic shocks. When random traumatic shocks occur during an ADT, it is possible that the degradation measurements cannot be taken afterward, which brings challenges to reliability assessment. In this paper, we propose an ADT optimization approach for products suffering from both degradation failures and random shock failures. The degradation path is modeled by a Wiener process. Under various stress levels, the arrival process of random shocks is assumed to follow a nonhomogeneous Poisson process. Parameters of acceleration models for both failure modes need to be estimated from the ADT. Three common optimality criteria based on the Fisher information are considered and compared to optimize the ADT plan under a given number of test units and a predetermined test duration. Optimal two- and three-level optimal ADT plans are obtained by numerical methods. We use the general equivalence theorems to verify the global optimality of ADT plans. A numerical example is presented to illustrate the proposed methods. The result shows that the optimal ADT plans in the presence of random shocks differ significantly from the traditional ADT plans. Sensitivity analysis is carried out to study the robustness of optimal ADT plans with respect to the changes in planning input.

Research Area(s)

  • Accelerated degradation tests (ADT), competing failure modes, degradation modeling, Fisher information, optimal design, reliability assessment