<p>The failure of critical components in large-scale, complex equipment such as hydraulic excavators often exhibit dynamic characteristics, including failure priority, sequence dependence, and functional interdependence. These coupled mechanisms complicate fault diagnosis and obscure the identification of failure propagation paths, presenting significant challenges for reliability assessment. To address these challenges, this study develops an advanced reliability evaluation framework based on a discrete-time Bayesian network (DTBN) to model and assess the dynamic failure processes of hydraulic excavators. The framework incorporates multi-state logic into dynamic failure modeling, enabling the integration of temporal and functional dependencies within a unified probabilistic structure. By incorporating dynamic Bayesian reasoning, the framework effectively evaluates system reliability, identifies fatigue-deformation wear synergistic failure as the primary failure mode, and validates the assessment results through a robust consistency analysis. Furthermore, a robustness evaluation method for parameter uncertainty under time-varying failure behavior is proposed, and the uncertainty propagation mechanisms of static and dynamic gates in sequential systems are analyzed. The proposed approach establishes a systematic, data-driven foundation for reliability assessment of hydraulic excavators operating under complex dynamic failure conditions.</p>