MODEL FOR TESTING MILITARY EQUIPMENT BASED ON EXTREME VALUES

In the present study, reliability theory is employed as the foundation for evaluating the overall operability of technical systems. Since the mechanized components of military equipment function under conditions of increased danger and high loads, it is essential to have a predictive tool for assessing the probability of system failure. Reliability theory, based on probabilistic- statistical methods, allows for the determination of the mean time between failures and the identification of moments when a test sample nears its endurance limit. This approach not only facilitates accurate modeling of system behavior under actual operational conditions by considering multiple influencing factors but also supports a comprehensive analysis of performance degradation over time. Furthermore, statistical models enable the evaluation of both the reliability of individual components and the overall probability of continuous, faultless operation of military equipment over extended periods. Additionally, incorporation of historical performance data enhances the robustness of these predictions, ensuring a reliable estimation of system endurance. To assess the reliability of military equipment under near-limit conditions, a mathematical model for extreme value testing is proposed. This approach is designed to pinpoint the most critical scenarios where the system operates at the edge of its endurance. The scenarios formulated within this model include verifying the compliance of the tactical and technical characteristics (TTC) of the military equipment test sample. The developed mathematical model for extreme value testing establishes threshold indicators by conducting tests across a series of scenarios, thereby predicting potential failures and evaluating the impact of each stress level on overall system reliability. Furthermore, this method integrates computational algorithms to simulate and analyze stress responses under variable operational conditions, thereby refining the precision of failure predictions. Moreover, this extreme value testing framework significantly improves both the efficiency and reliability of the evaluation process, ensuring that any critical vulnerabilities are promptly identified and managed under real-world conditions. It provides decision-makers with insights to mitigate risks and optimize maintenance schedules, ultimately contributing to enhanced operational readiness. This comprehensive approach lays the groundwork for future advancements in predictive maintenance strategies and dynamic system assessment in high-risk environments.