Bayesian reliability acceptance sampling plan sampling plans under adaptive accelerated type-II censored competing risk data

TL;DR

This paper develops a Bayesian reliability acceptance sampling plan using adaptive accelerated life testing under competing risks and type-II censoring, optimizing cost and reliability decisions.

Contribution

It introduces an adaptive Bayesian sampling plan under competing risks with step-stress testing, optimizing Bayes risk with a novel algorithm.

Findings

The proposed adaptive BRASP outperforms conventional plans in cost-effectiveness.

The methodology effectively incorporates competing risks and adaptive stress increases.

Real data illustration validates the practical applicability of the approach.

Abstract

In recent times, products have become increasingly complex and highly reliable, so failures typically occur after long periods of operation under normal conditions and may arise from multiple causes. This paper employs simple step-stress partial accelerated life testing (SSSPALT) within the competing risks framework to determine the Bayesian reliability acceptance sampling plan (BRASP) under type-II censoring. Elevating the stress during the life test incurs an additional cost that increases the cost of the life test. In this context, an adaptive scenario is also considered in that sampling plan. The adaptive scenario is as follows: 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. The Bayes decision function and Bayes risk are derived for the general loss function. An optimal BRASP under that adaptive SSSPALT is obtained for the quadratic loss function by minimizing Bayes risk. An algorithm is provided to determine the optimal proposed BRASP. Further, comparative studies are conducted between the proposed BRASP, the conventional non-accelerated BRASP, and the conventional accelerated BRASP under type-II censoring to evaluate the effectiveness of the proposed approach. Finally, the methodology is illustrated using real data.