Analysis of failure data for estimating availability and reliability indicators using statistical and stochastic methods

This paper presents a novel implementation of statistical and stochastic methods for estimating and evaluating reliability and availability indicators in technical systems. Using empirical failure data from a real-world military transport system, we introduce an innovative 7-state model that provides a detailed representation of operational phase of the systems. The research integrates Markov and semi-Markov processes to accurately model state transitions, particularly addressing scenarios where traditional Markov models are insufficient due to non-exponential state distributions. Our findings demonstrate that both statistical and stochastic methods yield closely aligned reliability and availability indicators, validating the robustness of the proposed methodologies. This research not only advances the accuracy of reliability assessments but also identifies actionable improvements to enhance operational readiness. They provide a comprehensive framework for analyzing and improving the operational efficiency of technical systems, with broader applications in various engineering fields.