# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 1 Introduction  
Ships that carry large amounts of cargo and passengers are larger than other modes of transportation, incurring higher construction costs. Most of them are foreign flagged, which are typically slow and operate for more than 20 years at sea, far from coasts. Consequently, their operating costs, including labor, fuel, and maintenance, are approximately two to three times their construction costs, depending on ship type and size [1, 2, 3, 4]. The unexpected operational interruptions of ships incur significant economic losses. Thus, shipping companies establish and operate maintenance systems that consider the operational characteristics of their ships for stable navigation. Because maintenance methods and levels significantly affect management costs, shipping companies strive to minimize management costs through optimal ship operation and efficient maintenance [5, 6, 7, 8, 9, 10, 11, 12]. However, current preventive maintenance systems, which are based on uptime cycles, are excessively costly. Therefore, a more efficient maintenance system is required for stable, economical ship operation while avoiding service interruptions. Researchers are attemptingto equip ships with predictive maintenance technology, which is used in various industries and other modes of transportation. It involves collecting and analyzing data on the operating condition of machinery in real time using artificial intelligence to identify operational abnormalities and predict the maintenance time [2; 3; 4; 6; 8; 10; 11; 13; 14]. However, ships perform preventive maintenance, hindering the collection of data on operational abnormalities, and operational characteristics limit data collection, increasing the difficulty of practical research. [PERSON] et al. [6; 8], [PERSON] et al. [9], and [PERSON] et al. [10; 11] detected abnormal conditions by analyzing data collected from ship engines using machine learning and establishing abnormality criteria [6; 8; 9; 10; 11]. However, the research was conducted using data before and after major failures that stopped engine operation, or by analyzing key monitoring data for engine maintenance, establishing thresholds, and detecting abnormal operations by analyzing data collected during normal navigation; engine combustion mechanisms and the dynamic marine environment were not considered. In addition, several recent studies, including those by [PERSON] et al. [15] and [PERSON] et al. [16], have attempted to collect and analyze operational data from ship engines to improve the efficiency of anomaly detection and fault diagnosis. However, research on predicting maintenance time after anomaly detection remains limited and underdeveloped in the maritime domain. As a result, the practical implementation of predictive maintenance is still challenging, especially under normal navigation conditions where identifying true anomalies in real time is inherently difficult. These limitations highlight the need for further improvements in predictive strategies tailored to the unique operational characteristics of marine engines. [PERSON] and [PERSON] [2; 3; 4] developed anomalous symptom detection algorithms for propulsion and generator engines (GEs) that can detect anomalous symptoms considering the engine status and operating conditions during normal operation, when the engine parameters are below the manufacturer's recommended thresholds for exhaust gas and lubricant temperatures [2; 3; 4]. However, current studies on the predictive maintenance of ships mostly focus on detecting anomalous symptoms. Practical predictive maintenance requires (1) criteria for determining when to perform maintenance after detecting anomalous symptoms and (2) predictive methods for the maintenance time. Therefore, in this study, we developed an algorithm for predicting the maintenance time of ship GEs using the revision GE condition criterion value (RGCCV) and the cylinder exhaust gas temperature, which were proposed in a previous study [4].