ANALYSIS OF LIFETIME DISTRIBUTIONS IN RELIABILITY ENGINEERING: CHALLENGES, ADVANCED METHODOLOGIES, AND FUTURE DIRECTIONS

Abstract

Reliability engineering is fundamentally dependent on the accurate analysis of lifetime distributions to predict and manage the lifespan of products and systems. This paper addresses the critical challenges persistent in this field, notably the difficulty in selecting the most appropriate distribution , the handling of complex censored data , the scarcity of real-world data , and the issues surrounding population heterogeneity and competing risks. We propose a framework utilizing advanced methodologies, including Bayesian techniques (MCMC) for small, censored datasets , copula-based models for competing risks , and integration of real-time data streams for repairable systems. By developing and testing these novel statistical approaches using a mixed-methods methodology, this research aims to enhance the robustness, flexibility, and practical applicability of lifetime distribution analysis across diverse sectors, including manufacturing, aerospace, and finance.