Bayesian reliability acceptance sampling plans under adaptive simple step stress partial accelerated life test

TL;DR

This paper develops Bayesian reliability acceptance sampling plans under an adaptive simple step-stress partial accelerated life test, optimizing decisions to reduce costs while maintaining reliability assessments.

Contribution

It introduces an adaptive SSSPALT framework with Bayesian decision-making, deriving optimal sampling plans and providing an algorithm for their computation.

Findings

Optimal Bayesian sampling plans are derived for quadratic loss.

The proposed adaptive plans outperform conventional methods in cost and efficiency.

Comparative analysis demonstrates the effectiveness of the adaptive approach.

Abstract

In the traditional simple step-stress partial accelerated life test (SSSPALT), the items are put on normal operating conditions up to a certain time and after that the stress is increased to get the failure time information early. However, when the stress increases, an additional cost is incorporated that increases the cost of the life test. In this context, an adaptive SSSPALT is considered where the stress is increased after a certain time if the number of failures up to that point is less than a pre-specified number of failures. We consider determination of Bayesian reliability acceptance sampling plans (BSP) through adaptive SSSALT conducted under Type I censoring. The BSP under adaptive SSSPALT is called BSPAA. The Bayes decision function and Bayes risk are obtained for the general loss function. Optimal BSPAAs are obtained for the quadratic loss function by minimizing Bayes risk. An algorithm is provided for computation of optimum BSPAA. Comparisons between the proposed BSPAA and the conventional BSP through non-accelerated life test (CBSP) and conventional BSP through SSSPALT (CBSPA) are carried out.