## 1 Introduction  
Ships are a means of transporting cargo and passengers in large quantities and generally operate for a long time, 20-30 years, after construction. And the management costs (e.g., operating and maintenance costs) required until the ship is abandoned are higher than the cost of construction [1; 2; 3]. Depending on the type of ship, the maintenance costs for parts such as engines and machinery account for 10-30% of the total management costs of the ship [1; 2; 3; 4]. In addition, the actual financial burden related to maintenance is greater when considering improper maintenance and unforeseen breakdowns and disruptions to ship operations due to repairs [4]. Therefore, an efficient maintenance process is essential for the economical and reliable operation of ships [3; 4; 5; 6; 7; 8; 9; 10].  
Predictive maintenance (PdM) has been studied for propulsion engines with high maintenance costs in ships. PdM detects the performance and condition of machines and systems to plan for cases of machine failure and maintenance, making it possible to maintain the best operation status and improve economic efficiency along with operational efficiency [11; 12; 13; 14]. PdM has been utilized in public transportation, such as aircraft [15; 16].  
As shown in Table 1, although previous studies detected frequent load fluctuations, which rely on the operational characteristics and environmental conditions of ship engines, they could not detect abnormal operating conditions due to actual engine abnormalities [6; 7; 9]. Therefore, there are limitations in using the results of existing studies for PdM. Accordingly, a methodology is required that can intuitively detect any anomalistic symptoms that cause failures and defects during normal operating conditions, rather than making predictions immediately before the occurrence of failures, by considering various and rapidly changing load fluctuations on ships and the variability in their normal opera tion data. Additionally, to implement PdM through real time data acquisition on a ship, a method is required to efficiently determine the engine status with minimal factors.  
-- Proposed a statistical method to support maintenance decisions by monitoring the anomalous status of the engine by classifying parameters that can cause abnormalities using a bootstrap based T2 multivariate chart of data acquired from the propulsion engines of ships.  
-- It classifies the collected engine data into five groups with similar characteristics through statistical analysis, prepares normal standards for each group, sets an upper limit to detect an abnormal state of the engine, and monitors the engine condition.  
In this study, propulsion engine data such as revolutions per minute (RPM), power, engine load, exhaust gas temperature, lubricant and coolant temperature, cylinder pressure, and scavenge air temperature were acquired from the alarm monitoring system (AMS) of a ship and preprocessed based on the characteristics of the propulsion engine control mode. Further, the AMS data and maintenance items related to the propulsion engine were analyzed to select data that can determine the anomalistic symptoms of a propulsion engine. Thereafter, the selected cylinder exhaust gas temperature (CET) and maximum cylinder combustion pressure (CCP) for the intuitive detection of anomalistic symptoms in the propulsion engine were employed to define the main engine condition criterion value (MCCV). To improve the efficiency of anomalistic symptom detection, a propulsion engine anomalistic symptom judgment algorithm was established using the revised MCCV (RMCCV) reflecting engine operating conditions and average data based on the voyage. Subsequently, an algorithm for detecting anomalistic symptoms during normal propulsion engine operation was developed and verified using machine learning, affording a practical engine anomalistic symptom detection algorithm applicable to ship PdM.