## 1 Introduction
### Literature Review
[16] analyzed maintenance practice data from 115 enterprises using hydraulic systems by employing machine learning models such as KNN, Ridge, and SVR. Through model agnostic feature importance analysis, they identified variables with significant impacts on system availability and sustainability and further established a hypothesis testing mechanism to interpret the roles of these variables. This study emphasizes the importance of interpreting variable contributions within multidimensional data, which is particularly critical for multi-source data modeling in marine lubricating oil systems, due to the large number of variables and high data noise. Multidimensional data analysis is central to robust fault diagnosis and reliability evaluation, laying a solid foundation for advancing dynamic Bayesian network methodologies. Selecting feature parameters from the signals of the ship's lubrication system is crucial for conducting reliability analysis. [PERSON] et al. [17] proposed a method for operational reliability assessment based on the operational state information of mechanical equipment. They established a mapping relationship between equipment operational state information and reliability, calculated operational reliability, and introduced a reliability assessment method for equipment operation under small sample conditions, using normalized wavelet information entropy and damage quantification for identification. [PERSON] et al. [18] proposed a second-order model analysis method, performing finite element analysis on a local model with boundary conditions derived from overall calculations, and conducted a reliability analysis for high-speed, high-power diesel engines. [PERSON] et al. [19] established a relevant prediction model using a Markov chain based on gear transmission data, and employed mathematical methods to predict the reliability of gear transmissions. [PERSON] [20] utilized a local cutspace arrangement algorithm to reduce the dimensionality of high-dimensional feature vectors extracted from vibration signals. They then applied a chaotic particle swarm optimization algorithm to estimate parameters for the proportional hazards model (PHM), completing a reliability analysis for structural components of train drive systems. [PERSON] et al. [21] determined the feature parameter set required for reliability analysis of gearbox performance degradation through experimental verification. [PERSON] et al. [22] extracted distance evaluation factor values from the frequency band energy using wavelet packet analysis, and used these as response covariates for the Weibull proportional hazards model in gear reliability assessment. [PERSON] et al. [23] applied distance evaluation methods for dimensionality reduction on bearing fault data, achieving objective identification of fault feature parameters for bearing fault recognition. [PERSON] et al. [24] introduced a new fault feature extraction method for rotating machinery based on adaptive multi-wavelet and distance evaluation indices, improving fault diagnosis accuracy. Selecting feature parameters from the signals of the ship's lubrication system is crucial for conducting reliability analysis. Single feature parameters are insufficient to reflect the overall degradation characteristics of the lubrication system, while an excessive number of parameters can affect the efficiency of subsequent reliability analysis. Existing dynamic Bayesian theory faces challenges in accurately reflecting the system's actual operating conditions when analyzing multi-sensor parameters of the lubrication system [25].