# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 1 Introduction
This technique can issue pre-failure alerts before abnormal performance parameters emerge, thereby preventing potential malfunctions. The implementation of this technology enhances navigation safety and reliability while providing theoretical foundations and technical tools for intelligent ship maintenance systems. During ship navigation, the vessel is not always in a steady state. Monitored data from navigation states such as acceleration, deceleration, and shutdown cannot characterize the overall performance of marine diesel main engines but rather reduce the accuracy of performance evaluation. Therefore, stable running intervals must be identified prior to analysis, with only steady-state navigation data used for performance assessment. Current mainstream steady-state detection algorithms fall into quantitative and qualitative categories. Qualitative methods include the confidence interval method, R-test, combined statistical testing, and SSD algorithm, while quantitative approaches encompass physics informed neural networks (PINN) and modified adaptive polynomial filtering algorithms. The combined statistical test and confidence interval method proposed by [PERSON] [8] in the 1980s detects steady states by comparing dual means and variances. However, its assumption of zero-mean normal distribution makes it vulnerable to interference in practical applications with insufficient robustness. The R-test improves F-test through variance ratio analysis, yet its threshold is significantly affected by filtering parameters and shows sensitivity to random noise with poor stability. Physics-informed neural networks integrate physical equations (e.g., partial differential equations, conservation laws, etc.) into loss functions to jointly optimize data fitting and physical constraints but exhibit convergence instability for high-dimensional nonlinear problems [9]. The robust adaptive polynomial filtering algorithm (ASF) based on M-estimation detects states through threshold parameter comparison, offering advantages of fewer parameters and strong noise resistance, yet requires manual parameter setting and shows poor adaptability to steady-state fluctuations [10]. In contrast, [PERSON] et al. used the SSD algorithm, hich constructs threshold criteria using Student's \(t\)-test within window widths and requires only two parameters (window width and significance level), while demonstrating superior performance in multivariate collaborative detection [11]. Given that marine diesel main engine stability identification involves coupled analysis of multiple thermodynamic parameters, this study adopted the SSD algorithm to identify stable operating intervals. Its concise parameter system and multivariate compatibility effectively support steady-state discrimination requirements under complex operating conditions. Current research on fault prediction can generally be categorized into two approaches: state prediction and state classification. State prediction, constrained by human factors and data quality, suffers from insufficient model accuracy due to challenges in realistically simulating diesel engine operating conditions and quantitative/qualitative modeling with scarce samples. For instance, [PERSON] et al. developed a CNN-BiGRU-based exhaust temperature prediction model for marine diesel engines in which only exhaust temperature data were utilized for prediction. By artificially linearly adjusting the dataset to simulate abnormal exhaust temperature changes when potential faults occur in diesel engines, the model revealed prediction errors [12]. [PERSON] et al. innovatively proposed a PCA-CNN-BiLSTM hybrid model, which employs only two parameters (after PCA dimensionality reduction) to represent overall engine performance and considers solely stable operating conditions of a specific diesel engine model with minimal load variations [13]. Consequently, such prediction models struggle to establish corresponding fault prediction frameworks for complex, multivariate, and correlated fault systems [14].