# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 1 Introduction  
As the core power generation unit of marine vessels, marine main diesel engines are prone to frequent failures due to prolonged high-load operations under harsh marine conditions. Performance evaluation of these engines enables early prediction of impending failures and proactive warnings, thereby allocating sufficient time for corrective maintenance or preventive interventions. However, the structural complexity and variable operational conditions of marine diesel main engines present significant challenges in achieving reliable performance assessments. Conventional onboard data acquisition systems typically record limited operational parameters that insufficiently reflect the engine's actual performancestate, while critical deep-seated condition parameters remain unmonitored. All measurable parameters exhibit operational condition dependency, with early-stage performance degradation often manifesting as subtle fluctuations in these parameters. Such incipient anomalies are frequently obscured by strong background noise interference, rendering traditional threshold-based warning methods inadequate due to insufficient sensitivity and high false-alarm rates. With the advancement of big data and communication network technologies, new requirements have been proposed for the intelligent operation and maintenance of marine diesel engines. The concept of the \"smart ship\" was first introduced by Det Norske Veritas (DNV) in its _Future Shipping Industry_ report in 2014, which significantly promoted global research in this field [1]. In 2015, the China Classification Society (CCS) released the world's first smart ship code, marking a major step forward for China in the development of smart ship technologies [2]. Subsequently, in 2017, Lloyd's Register published the _Design Rules for Intelligent Ship Systems_, further providing technical guidance for this domain [3]. To accelerate the development of intelligent ships, Prognostics and Health Management (PHM) technologies have been gradually introduced into the maritime industry [4]. Data-driven PHM techniques establish fault prediction models by analyzing large volumes of historical data and use real-time equipment data for health management. From early sensor and data logging systems to today's cloud computing and artificial intelligence technologies, PHM has continuously improved the reliability and availability of equipment [5] while becoming increasingly intelligent and autonomous. [PERSON] et al. investigated the application of PHM in marine diesel engines, described the characteristics of traditional fault diagnosis methods, proposed a system architecture for diesel engine PHM, summarized the key enabling technologies, and provided insights into data analysis, fault diagnosis, early warning, and health management [6]. It is evident that health assessment and fault diagnosis of marine diesel engines based on monitoring data, intelligent algorithms, and condition monitoring have become an inevitable trend in the intelligent operation and maintenance of ship propulsion systems. The core objective of intelligent marine diesel engine management lies in the real-time monitoring, evaluation, and analysis of engine health and operational performance to enable timely fault prediction. This technological pathway primarily relies on big data and information technologies to uncover latent correlations among performance parameters through data mining, allowing for accurate identification of both visible and latent fault features. Such approaches significantly enhance the level of intelligent decision making for marine diesel engines [7]. Based on this objective, this study conducts research utilizing monitoring data to analyze and assess the health condition of marine main diesel engines. It quantifies the overall engine performance to visually observe operational trend variations and proposes a fault early-warning technique according to the degradation trend of holistic engine performance.