## 1 Introduction
### Background and Literature Review
In [11], the dynamic reliability model of the hydraulic system is established by using the continuous-time T-S Dynamic fault tree to solve the fault rate of the system, and the results are compared with the traditional analysis method and the probability importance and key importance of the system unit are calculated. Bayesian networks (BN) are increasingly used in system safety and reliability analysis [12]. As a graphical inference method, Bayesian networks represent the causal relationships between events. A BN has some characteristics and advantages over reliability methods such as fault tree analysis, Petri nets, Markov chains, etc. A BN can be used to predict the probability of unknown variables or update the probe of known variables by evidence to achieve two-way reasoning [13]. A dynamic Bayesian network (DBN) carries out extensions on dynamic attributes such as time based on a BN [14] and combined with the state transfer probability table of the components to establish a dynamic reliability model of the system, giving full play to the advantages of BN while achieving dynamic reliability analysis of the system [15]. BN construction focuses on determining the network structure and conditional probability table. The usual method is to transform the traditional FT model of the system into a BN model to achieve BN construction, but some drawbacks in the traditional FT still pass into the BN. Due to the power of Bayesian networks, they have a wide range of applications in the field of reliability analysis. In [16], the author proposes a Bayesian network modeling framework that systematically combines design life estimates, operational data, and expert judgment for the reliability prediction of aircraft subsystems. The model predicts the reliability of a large aircraft fleet by using failure and maintenance data provided by a large fleet operator. In [17], reliability characteristics such as failure probability, failure rate, and mean time to failure of the floating offshore wind turbine are determined according to the Bayesian network predictive analysis. In [18], the fault tree mapping dynamic Bayesian network (DBN) method is applied to the reliability research study of centrifugal compressor units, and its usability and reliability are evaluated dynamically. In [19], in order to deal with epistemic uncertainty and dynamic characteristics in the reliability assessment process of controllable pitch propeller hydraulic systems, the D-S evidence theory and dynamic Bayesian network were applied to establish a novel approach for assessing its reliability and availability. In [20], to deal with the uncertain knowledge and various information in the safety assessment, characteristic indicators are extracted from marine environment systems and discretized with the Cloud model. The dynamic evaluation and risk zoning of navigation safety is realized based on Bayesian probabilistic reasoning and Dempster-Shafer (DS) evidence theory. In [21], the author develops a new dynamic Bayesian network (DBN) framework for fault diagnosis and reliability analysis of OWT gearbox systems by incorporating components' degradation information and a condition-based maintenance (CBM) strategy. The reliability, availability, and mean time between failures (MTBF), as well as the failure criticality index (FCI) for each subassembly, are estimated.