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SUMMARY & CONCLUSIONSThis paper presents an advanced methodological framework for reliability analysis by integrating dynamic fault tree (DFT) modeling with phase-type (PH) distributions, further enhanced by probabilistic model checking techniques. DFTs are widely employed for representing complex system behaviors, such as functional dependencies and order-dependent failures, that standard static fault trees cannot adequately capture. While DFTs provide sophisticated modeling capabilities, translating them into explicit probabilistic representations for precise failure time analysis remains challenging. Phase-type distributions offer a compelling solution to this challenge. Defined by the absorption times of finite-state continuous-time Markov chain (CTMC) processes, PH distributions can approximate any positive distribution arbitrarily closely. This paper explicitly demonstrates how the failure time distributions derived from the DFT inherently conform to PH distributions. This theoretical connection provides a rigorous and systematic approach to mapping various DFTs into their corresponding PH distribution models. In particular, the link between DFT and PH distributions enhances the interpretability of the system failure distribution with DFT basic events. The new framework enables the use of arbitrary PH distributions to approximate any system lifetime distribution. Furthermore, the efficient translation from DFT to CTMC enables rigorous, automated probabilistic model checking, which significantly enhances the analytical power and efficiency of DFT analysis by allowing for the precise computation of reliability metrics, such as system unreliability, mean-time-to-failure (MTTF), and other critical dependability measures.