Latest revision as of 10:33, 27 January 2026

Abstract

Accelerated life testing has become essential in modern reliability assessment, particularly for high-reliability products where traditional testing is often impractical due to time constraints. This study introduces a comprehensive framework for reliability analysis using Weibull-based partially accelerated life tests under a unified hybrid censoring scheme. Assuming lifetimes follow a Weibull distribution, we provide both classical and Bayesian estimation procedures to estimate core quantities, shape, scale, and the acceleration factor, alongside reliability metrics at normal operating conditions. The inferential framework encompasses point estimation along with uncertainty quantification, using both approximate confidence intervals and Bayesian credible intervals derived from Markov Chain Monte Carlo methods. A comprehensive Monte Carlo simulation study evaluates the performance of various methods in terms of mean squared error, interval coverage, and average interval width across a range of censoring patterns. The results provide actionable insights and practical recommendations for selecting appropriate methods and designing future studies under different censoring scenarios. The proposed methodology is further illustrated through two real-world case studies: the reliability of white organic light-emitting diodes and the lifetime of micro-droplets in ambient environments. These examples highlight the method’s flexibility and practical relevance in both engineering and biomedical reliability applications.OPEN ACCESS Received: 31/07/2025 Accepted: 10/09/2025 Published: 23/01/2026

Document