To address the complex role of equipment support resource allocation on mission reliability during warship missions, this study establishes a warship mission reliability simulation system by using agent-based modeling to simulate warship task flow, equipment reliability structures, fault rules, and equipment support resource allocation schemes. The simulation system has the characteristics of multielement, modular, flexible configuration and easy operation. The key metrics relevant to different task conditions, such as the mission reliability of multistage and multistate systems, and the number of recommended allocations of equipment support resources, can be calculated by the system. In addition, sensitivity analyses of task parameters, equipment reliability structure, and the number of spare parts is carried out, and the effects on task reliability are discussed. The influencing factors considered in the modeling and calculation of mission reliability are expanded in this study, which can provide technical support for the optimal allocation of shipboard equipment support resources during the use phase of a warship, and the supportability design of warship equipment during the development phase.

The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

In response to the limitations of traditional static reliability analysis methods in characterizing the reliability changes of the Integrated Power System, this paper proposes a time-varying reliability analysis framework based on a Dynamic Bayesian Network. By embedding a multi-physics coupled degradation model into the conditional probability nodes of the Dynamic Bayesian Network, a joint stochastic differential equation for the degradation process was constructed, and the dynamic correlation between continuous degradation and discrete fault events throughout the entire life cycle was achieved. A modified method for modeling continuous degradation systems was proposed, which effectively solves the numerical stability problem of modeling complex degradation systems. Finally, the applicability and correctness of the model were verified through numerical examples, and the results showed that the analysis framework can be effectively applied to time-varying reliability assessment and dynamic health management of complex equipment systems such as the Integrated Power System.

Maritime kill webs integrate heterogeneous platforms across air, surface, and underwater domains, with each platform potentially supporting multiple mission functions. Existing reliability models often represent attacks as complete node removals and therefore cannot distinguish overall platform failures from local functional degradation or quantify reliability losses caused by cross-domain coordination. This study develops a two-layer platform–function model and a probabilistic attack-response mechanism that couples platform-level and function-level damage. Multi-domain adaptation coefficients are introduced to modify functional-edge reliability, and system degradation is assessed using functional-node retention, structural closure, effective closure, and mission reliability retention. In a simulation containing 24 platforms, 55 functional nodes, and 15,246 candidate kill chains, targeted attacks caused substantially greater mission degradation than random attacks. At a cumulative attacked-platform ratio of 0.25, mission reliability retention was 0.5340 under random attack, compared with 0.2173 and 0.2184 under degree-based and functional-carrier-prioritized attacks, respectively. Under functional-carrier-prioritized attack, reliability loss was greatest for underwater targets (0.869). Sensitivity and ablation analyses showed that local functional degradation strongly affects retained mission reliability, while multi-domain adaptation mainly affects whether structurally closed chains satisfy the mission-reliability requirement. The proposed framework distinguishes structural disruption from reliability-induced mission interruption in multilayer mission systems.