Design of reliability acceptance sampling plans

Abstract

A reliability acceptance sampling plan (RASP) is used for sampling and decision-making in the acceptance or rejection of a lot of products based on lifetime data obtained from a life test. In practice, censored life tests are employed due to limitations in cost, time, and other testing resources for the collection of lifetime data. This thesis develops the design of optimal RASPs under various censoring schemes and testing environments. Design of optimal Bayesian RASPs (BRASPs) are considered under interval censoring schemes (ICS) and hybrid censoring schemes using Bayesian decision-theoretic approaches. These models incorporate the adversarial relationship between manufacturers and consumers, who differ in prior beliefs and utility functions. Global market competitiveness and rapid technological advancement have pushed manufacturers to produce products with very high reliability. For such products, the mean time to failure under normal operating conditions is often prohibitively long. To address this issue, a BRASP based on a novel adaptive simple step-stress partial accelerated life test (ASSSPALT) framework is proposed under Type-I censoring using common prior and utility functions. The adaptive scheme dynamically adjusts stress levels based on observed failures, providing a general framework that accommodates both accelerated and non-accelerated testing under Type-I censoring. For complex products, failure may occur due to multiple causes. The work considers the design of RASP for competing risk data under progressive Type-I interval censoring using the producer's and consumer's risk approaches. The asymptotic properties of maximum likelihood estimators are derived to develop optimal plans. A frailty-based model is employed to capture dependence among competing risks and evaluate its influence on sampling plan performance. Subsequently, RASP is extended to a Bayesian framework under interval censoring. Further, for complex products with very high reliability, the design of BRASP is considered based on ASSSPALT under Type-II censoring for competing risk data. This framework unifies both accelerated and non-accelerated testing scenarios for competing risk data under Type-II censoring. The proposed methodologies for designing RASPs are illustrated using real-life data.