## 1 Introduction
### Aims and Objectives  
In the modern ship power system, the lubricating oil system is a key subsystem to ensure the stable operation of the main engine and auxiliary equipment, and its reliability is directly related to the operation safety and life of the ship equipment. However, due to the ship lubrication system usually operating in strong interference, high load, and variable operating conditions, its complex structure includes multiple types of key components and a large amount of sensor data, which makes the traditional reliability assessment methodsface many challenges in dealing with dynamic degradation, multi-source data fusion, and complex dependence between states. The traditional static model cannot reflect the dynamic evolution process of the system running state, and the simple threshold judgment cannot effectively deal with the noise, uncertainty, and fuzzy boundary in the signal. In addition, the existing methods strongly rely on expert experience for state recognition and health assessment, and lack scalable and data-driven modeling mechanisms, which are difficult to adapt to the increasing needs of the intelligent operation and maintenance of ship equipment. In order to solve the above problems, this paper aims to construct a reliability evaluation method of marine lubricating oil systems based on an improved dynamic Bayesian network and multi-source data fusion. From system modeling, signal processing, feature extraction, state recognition, probabilistic modeling to system level reasoning, a whole process method framework running through the data layer and decision layer is proposed. The goal of this study is to achieve the accurate identification of the lubricating oil system operating state, dynamic reliability prediction, and operation and maintenance strategy optimization. The experimental results show that the proposed method not only has good accuracy in instantaneous availability evaluation but also shows strong scalability and practicability in multi-state complex systems, which provides reliable data support and decision-making basis for the health management and fault prevention of ship critical systems. Finally, this paper hopes to promote the traditional reliability analysis from static modeling to dynamic, data-driven, and intelligent direction, and provide theoretical basis and engineering practice value for the efficient and safe operation of ship equipment. Focusing on the reliability evaluation problem of marine lubricating oil systems in a complex operation environment, this study proposes a multi-level analysis method that integrates multi-source signal processing, feature dimension reduction, and a dynamic Bayesian Network (DBN) enhancement mechanism, and constructs a dynamic reliability modeling framework from equipment level to system level. This paper aims to realize the accurate identification, health assessment, and maintenance decision support of a lubrication system. In the second part, the Methodology, the simulation model of the lubricating oil system is constructed by AMESim software 2021.1, the EEMD algorithm is used to denoise the multi-source sensor signals, and the nonlinear time-varying features are extracted by combining wavelet packet analysis, and the automatic identification of the operation state is realized based on K-means clustering. In the aspect of system reliability modeling, a Dynamic Bayesian Network (DBN) based on a fault tree structure was constructed to quantify the probability transition relationship of equipment state evolution over time, and distinguish the state transition rate under the situation with and without maintenance. Furthermore, the proportional hazards model (PHM) is integrated to model the independent life of each key component, and the optimal failure distribution (Weibull, exponential, or normal) is selected according to the historical life data.