## 3 Experimental Results
### Dimensionality Reduction Preprocessing of Time Domain and Frequency Domain Feature Parameters
of the marine oil system with maintenance during navigation was higher than that of the marine oil system without maintenance during navigation. Therefore, maintenance during navigation can effectively improve the reliability of the marine oil system. In order to accurately evaluate the reliability evolution characteristics of the ship main oil system under the condition of no maintenance, this paper constructs a Dynamic Bayesian Network (DBN) model based on system functional logic. The proposed method is compared with the traditional static Fault Tree Analysis (FTA). By modeling the failure rate of key components (including the main oil system GUB1 and the oil pump PUB1), the reliability curves of their evolution with the running time are drawn as shown in Figure 9.  
From the comparison results, it can be seen that the DBN method is significantly better than the traditional FTA method in terms of system modeling accuracy, and can more truly capture the dynamic redundancy strategy and state transition mechanism of the system. Taking the main oil system GUB1 as an example, its reliability at the end of the operation cycle (e.g., \(4\times 10^{4}\) h) is 0.261 in the DBN model but only 0.169 in the traditional fault tree model, with an improvement of 9.2%. The results show that the DBN can more accurately model the logic of the cold standby pump starting up after the failure of two running pumps, thereby avoiding the error of the traditional method that the standby pump is continuously involved in the failure process. Similarly, for the slip-oil pump PUB1, the modeling advantage of the DBN method is more significant. Under the same running time limit, the reliability of the proposed method is improved from 0.207 through the FTA method to 0.349, and the improvement is as high as 14.2%. This reliability improvement is attributed to the advantages of a DBN in modeling the multi-state characteristics of the oil pump and its backup logic, especially when considering the nonlinear and non-independent state transition relationship, which is more expressive. In addition, the DBN supports dynamic reasoning on the time evolution of the system, which provides more reliable data support for preventive maintenance and condition prediction of the oil system. In practical ship operations, Scheduled Maintenance (SM) is a critical strategy to ensure high system availability. To analyze the effect of maintenance strategies on system reliability, this study further incorporates a maintenance rate parameter \(\mu\) into the DBN model, allowing for dynamic updates of state transition probabilities. Specifically, for a  
Figure 8: Comparison of system transient availability. component in a failed state, the probability of being repaired within a time interval \(\Delta t\) is expressed as follows:  
\[P[A(t+\Delta t)=1|A(t)=1]=1-e^{-\mu\Delta t} \tag{22}\]  
\[P[A(t+\Delta t)=0|A(t)=1]=e^{-\mu\Delta t} \tag{23}\]  
Based on the updated conditional probability tables (CPTs), the reliability evolution of the system under scheduled maintenance is derived. As shown in Figure 10, taking GUB1 as an example, after the implementation of an appropriate maintenance schedule, the reliability stabilizes around 0.9873 after approximately \(0.4\times 10^{5}\) h of operation, which is significantly higher than the final reliability of 0.24 without maintenance. This indicates that periodic maintenance can effectively slow down system degradation. In conclusion, the proposed DBN-based reliability assessment method demonstrates superior adaptability and accuracy compared to the traditional fault tree approach, particularly in handling redundancy logic, dynamic system behavior, multi-state modeling, and integration of maintenance strategies. This makes it particularly suitable for complex engineered systems such as the ship's lubrication oil system, offering strong support for health assessment and scientifically informed maintenance planning.