Bayesian Methods for Accelerated Destructive Degradation Test Planning

Date
2010-11-01
Authors
Shi, Ying
Meeker, William
Meeker, William
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract

Accelerated Destructive Degradation Tests (ADDTs) provide timely product reliability information in practical applications. This paper describes Bayesian methods for ADDT planning under a class of nonlinear degradation models with one accelerating variable. We use a Bayesian criterion based on the estimation precision of a specified failure-time distribution quantile at use conditions to find optimum test plans. A large-sample approximation for the posterior distribution provides a useful simplification to the planning criterion. The general equivalence theorem (GET) is used to verify the global optimality of the numerically optimized test plans. Optimum plans usually provide insight for constructing compromise plans which tend to be more robust and practically useful. We present a numerical example with a log-location-scale distribution to illustrate the Bayesian test planning methods and to investigate the effects of the prior distribution and sample size on test planning results.

Comments

This preprint was published as Ying Shi and W.Q. Meeker, "Bayesian Methods for Accelerated Destructive Degradation Test Planning" IEEE Transactions on Reliability (2011): 245-253, doi: 10.1109/TR.2011.2170115.

Description
Keywords
Citation
DOI
Source
Collections