# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 1 Introduction
This study addresses this issue by adopting state classification with statistical data analysis methods to process multi-dimensional real-ship monitoring data, capturing temporal variations in statistical features while eliminating subjective influences, followed by autonomous operating condition partitioning. Current mainstream data analysis methods include principal component analysis (PCA), data mining clustering algorithms, and time series analysis. For example, [PERSON] et al. used PCA to extract two feature parameters that explained more than half of the characteristics in the sample data [13], while [PERSON] et al. derived performance parameters through coefficient and correlation efficiency definitions, established initial diesel engine performance maps via surface fitting, and tracked performance degradation trends by comparing real-time and initial performance maps [15]. Fault warning methodologies are divided into physics-based models and data-driven approaches. Physics-based methods require precise mathematical or physical models to describe equipment operation [14]. However, under dynamic load conditions, these models often produce larger errors, resulting in systemic biases in the data. In contrast, data-driven approaches construct analytical models based on historical operational data and utilize algorithms to mine potential fault features, effectively circumventing the complexities of physical modeling. With the advancement of intelligent algorithms, data-driven methods have become the dominant research direction in fault prediction for marine diesel engines. The technical methods employed in this study include data mining clustering algorithms and time series analysis. Clustering algorithms are particularly suitable for solving complex operating condition partitioning in marine main diesel engines. For instance, [PERSON] et al. utilized the K-means clustering algorithm to partition engine operating intervals, followed by quadratic polynomial fitting to derive relationships between engine load and fuel consumption rates [16]. [PERSON] et al. applied the EM algorithm to identify the frequent operating ranges of marine diesel engines and analyzed engine performance using PCA [17,18]. [PERSON] et al. explored methods including K-means clustering, Gaussian mixture models, density-based clustering, self-organizing maps, and support vector machines after data dimensionality reduction, concluding that dimensionality reduction may cause information loss. Although these methods are straightforward to implement, they require parameter tuning and validation prior to practical application [19]. Time series analysis investigates statistical regularities in the temporal evolution of sequential data, enabling prediction and control of future events, such as preventing fault occurrences. [PERSON] et al. integrated time-domain analysis with deep learning algorithms, focusing on temporal characteristics of faults and resulting in higher diagnostic accuracy compared to traditional methods [20]. [PERSON] et al. proposed combining time-domain and frequency-domain analyses, achieving superior accuracy over frequency-domain methods alone [21]. In current research on marine diesel engine fault prediction, the use of single or limited data sources to evaluate overall engine performance often results in incomplete assessments and introduces systematic bias into model predictions. Moreover, existing studies that focus solely on predicting stable operating conditions for a specific diesel engine model fail to adequately support the evolving dynamic requirements of intelligent ship engine operation and maintenance. To address these limitations, this study selected 22 performance parameters of a marine diesel engine over an extended time span. By incorporating the SSD steady-state detection algorithm, a data-driven approach using the CLIQUE clustering algorithm is herein proposed for autonomous classification of multi-parameter operating conditions.