## 3 Experimental Results
### Comparison of This Paper with Other Fault Diagnosis Methods  
In the field of ship critical system reliability analysis, a variety of emerging technologies and methods have appeared in recent years, including neural network testing, system Theoretical Process Analysis (STPA) combined with Fuzzy cognitive maps (FCMs), cognitive uncertainty modeling of multi-state Bayesian networks, the AHP-FCE intelligent state assessment method, and the GO-Bayesian network model. These methods have their own characteristics and show strong fault diagnosis and prediction capabilities in different scenarios. Neural network methods have shown superior feature extraction and decision-making capabilities, especially in fields with high safety requirements such as autonomous driving and medical diagnosis. Their application in autonomous ship navigation and anti-collision nature enhance the robustness of the system by means of mechanisms such as adversarial testing and coverage testing. However, due to their \"black-box\" characteristics and difficult to interpret internal mechanisms, the application of neural network models in complex systems such as ship lubrication systems with high requirements for transparency and reliability still faces challenges. In contrast, the fusion method of the PHM model, dynamic Bayesian network, and cloud center of gravity model proposed in this paper not only has strong data processing ability but also can clearly show the state transition and causal relationship between components through Bayesian structure modeling, which has stronger interpretability and dynamic modeling ability. The combination method of STPA and FCM is based on control theory and is suitable for risk analysis between Autonomous Sailing Ship (MASS) functional systems, which reflects the failure probability of the system under steady-state conditions by identifying unsafe control behavior (UCA) and potential causes. This method is more suitable for function analysis at macro system level, especially for risk modeling at control logic level, but it has less ability to deal with real-time state changes and multi-source information fusion at the component level. In contrast, the proposed method not only quantifies the change in failure rate of a single component, but also integrates multi-state and multi-strategy information of the system to realize the dynamic reliability analysis from the component to the system level. Aiming at the epistemic uncertainty problem of multi-state Bayesian networks in practical engineering applications, some studies have combined the analytic hierarchy process (AHP) and triangular fuzzy number methods to try to reduce the subjectivity introduced by expert scoring. However, these methods still face the problems of high computational complexity and low reasoning efficiency when constructing large-scale conditional probability tables. This paper adopts the dynamic Bayesian network structure and introduces the fuzzy evaluation mechanism combined with the cloud center of gravity model, which effectively alleviates the epistemic uncertainty problem in conditional probability inference while maintaining the flexibility of modeling. Moreover, the dynamic update of historical operation data can improve the inference efficiency and accuracy. In addition, the AHP-FCE model shows good results in the state assessment of a ship seawater system, and the scientificity of the assessment is improved by combining the multi-level fuzzy comprehensive assessment after the state is identified by a neural network. However, this method mainly focuses on static state recognition and multi-stage information fusion, and has shortcomings in dynamic reasoning and system evolution modeling. The PHM-Dynamic Bayesian Network-Cloud model proposed in this paper not only realizes the life prediction and dynamic evolution simulation of unit components but also simulates the system availability change for different maintenance strategies.