## 5 Conclusions  
In this study, the following conclusions were inferred by developing and verifying an engine anomalistic symptom judgment algorithm using operational ship data and machine learning to employ PdM to ship propulsion engines.  
1. The CET and maximum CCP of the cylinder were selected as the data that can directly determine the engine condition by analyzing troubleshooting data, maintenance items, and collected data of the engine.
2. To intuitively detect the anomalistic symptoms of the propulsion engine, the MCCV was defined based on the CET and maximum CCP data.
3. The MCCV based anomalistic symptom judgment algorithm failed to determine anomalistic symptoms due to changing operating conditions, such as RPM and the marine environment.
4. To improve anomalistic symptom detection, the RMCCV, which reflects changing operating conditions and environments, was established.
5. \(\text{R}^{2}_{\text{adj}}\) between the CET of the RMCCV and correction factors was in the range of 0.754-0.857 in all cylinders. \(\text{R}^{2}_{\text{adj}}\) between the maximum CCP and correction factors was found to be in the range of 0.952-0.968, showing high explanatory power.
6. The MAE range of the CET according to the correction factor was 4.095-7.411, and the MAE of the maximum CCP was in the range of 1.188-1.867, showing excellent predictive performance compared to the data scale. Therefore, the developed RMCCV based anomalistic symptom prediction algorithm was reliable.
7. To reduce the impact of noise data and improve the predictability of anomalistic symptoms, the average data based on the VOYAGE were applied to the RMCCV based anomalistic symptom judgment algorithm, resulting in the effective anomalistic symptom determination of the propulsion engine.  
As described above, an algorithm for effectively determining propulsion engine anomalistic symptoms, suitable for ship PdM, was successfully developed. Given the diverse voyage standards for different routes and operational characteristics of ships, it is crucial to set a certain period, considering the operating characteristics of the target ship and engine, and integrate it into the RMCCV based algorithm.  
The future plan involves validating the developed anomalistic symptom judgment algorithm by applying it to operational ships to ensure its reliability. In addition, there are intentions to develop a PdM platform capable of real time anomaly detection in the engine and a function to identify corresponding maintenance items based on detected anomalistic symptoms. These initiatives will contribute to further improving ship maintenance practices and optimizing the PdM system for engines.  
**Author Contributions:** Conceptualization, J.P.; methodology, J.O.; investigation, J.P.; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.O. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NO. 2020R1I1A2073426).  
**Index Availability Statement:** Not applicable.  
This paper was developed from the Ph.D. thesis of the first author.  
The authors declare no conflict of interest.