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

2025-07-31

AI Summary

For highly reliable complex products with long lifetimes under normal operating conditions and multiple concurrent failure modes, conventional Bayesian reliability acceptance sampling plans (BRASPs) under Type-II censoring suffer from low efficiency. To address this, this paper proposes a novel BRASP framework integrated with adaptive accelerated life testing (AALT) based on a competing risks model. Innovatively, it combines step-stress partially accelerated testing with a dynamic stress adjustment mechanism, enabling real-time optimization of subsequent stress levels using early failure data. Within a Bayesian decision-theoretic framework, the method employs Type-II censored data and a quadratic loss function to minimize total risk and determine the optimal sampling plan. Empirical results demonstrate that the proposed approach significantly reduces test cost and duration while improving decision accuracy and economic efficiency, offering greater flexibility and engineering practicality compared to conventional non-accelerated and fixed-stress accelerated BRASPs.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Develop Bayesian reliability sampling for accelerated life testing
• Optimize adaptive stress testing under competing risks
• Compare proposed method with conventional sampling plans

Innovation

• Uses SSSPALT for Bayesian reliability sampling
• Implements adaptive stress increase strategy
• Optimizes BRASP with quadratic loss function