ABSTRACT Reliability assessment of complex engineering systems is crucial for ensuring safety, performance, and cost‐effectiveness. Traditional reliability models frequently treat failure rates as static and analyze component failure rates in isolation. When dependencies are acknowledged, they are typically considered in a linear manner. This approach, however, fails to adequately capture the dynamic and nonlinear interactions that characterize real‐world systems. This paper proposes a novel reliability modeling framework that integrates dynamic failure rates and nonlinear dependencies to enhance predictive accuracy in reliability analysis. The model considers time‐varying operational and environmental factors that influence failure behavior, along with nonlinear interdependencies among system components, which can amplify risks in unpredictable ways. The proposed framework improves reliability estimation under evolving conditions. To illustrate the implications of the proposed model assumptions, the model is applied on two numerical examples and to demonstrate the impact of the model assumptions, the reliability is compared with the case where these assumptions are not considered. The findings of this research contribute to the advancement of reliability engineering by providing a more comprehensive approach to modeling failures in dynamic, interconnected systems.
Karimjonov et al. (2026) studied this question.