RELIABILITY ANALYSIS OF COMPLEX SYSTEMS WITH STOCHASTIC DEPENDENCIES

Abstract

Reliability assessment of complex systems with interdependent components is crucial for ensuring their performance and minimizing unexpected failures. In such systems, the behaviour and reliability of individual components are influenced by stochastic dependencies, where failures or performance degradation in one component can propagate to others. This paper presents a comprehensive analysis of reliability in complex systems using stochastic modelling and analysis techniques. The study begins by discussing the importance of considering stochastic dependencies in reliability analysis and the challenges associated with modelling and analyzing such systems. Various mathematical models, including fault trees, reliability block diagrams, and Bayesian networks, are explored to capture the interdependencies among components. Stochastic processes, such as Markov processes and stochastic Petri nets, are introduced as powerful tools for characterizing the dynamic behavior of complex systems. Uncertainty quantification plays a crucial role in reliability analysis, as it enables the assessment of the impact of various uncertain parameters on system performance. The paper investigates probabilistic distributions for component failures, repair times, and environmental conditions to incorporate uncertainty into the reliability models. Techniques like Monte Carlo simulation and rare event simulation are employed to estimate the probability of system failure, mean time to failure, and other relevant reliability metrics. Sensitivity analysis is conducted to identify critical components or factors that significantly influence system reliability. By quantifying the sensitivity of reliability measures to changes in input parameters, decision-makers can focus on improving the reliability of crucial components or optimizing maintenance strategies to enhance overall system performance. Furthermore, the integration of reliability analysis with optimization techniques is discussed. This allows for identifying cost-effective strategies for system improvement, such as component redundancy allocation, maintenance scheduling, or performance optimization. The research presented in this paper contributes to a deeper understanding of reliability analysis in complex systems with stochastic dependencies. The insights gained from this analysis can aid in making informed decisions to enhance system reliability, mitigate risks, and optimize resource allocation in various industries such as transportation, energy, telecommunications, aerospace, and manufacturing. By considering the stochastic nature of dependencies, researchers and practitioners can ensure the robustness and resilience of complex systems in the face of uncertainties.