# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 4 Conclusions  
In this study, algorithms for predicting maintenance times for ship engines were developed and tested using data from an operating ship generator for practical predictive maintenance. An indicator of anomalous GE symptoms (RGCCV) and the cylinder exhaust gas temperature were used as algorithm bases, and the prediction performances of both algorithms were analyzed and compared. The highlights of this study are as follows:  
1. A GE maintenance time prediction algorithm was developed by defining the PTMRG equation, which predicts the maintenance time via regression analysis based on the RGCCV, and defining the PTROPRG equation, which predicts the remaining operating time until maintenance. 2. Another prediction algorithm was developed by defining the PTMET equation, which predicts the maintenance time via regression analysis based on the exhaust gas temperature of the GE's cylinders, and defining the PTROPET equation, which predicts the remaining operating time until maintenance. 3. The PTROPET-predicted maintenance times for all cylinders were, on average, 35.3 h later than those predicted using PTROPRG (by approximately 24%). PTROPRG (PTMRG) is the time predicted using values (RGCCV) adjusted for the status based on the engine operating environment, reflecting the operating status of each cylinder. This approach is more practical and reliable than predicting the maintenance time using changes in the exhaust gas temperature of the cylinder, which is presently the criterion suggested by engine manufacturers for maintenance decisions. 4. The comparison of prediction performance between the PTMRG and PTMET showed that the MAE, MSE, and RMSE values for all cylinders indicated better prediction \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item\textbackslash{}Cylinder** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multicolumn{8}{c}{PTMRG} \\ \hline MSE & 10.358 & 11.789 & 18.594 & 14.415 & 25.331 & 18.852 \\ RMSE & 3.218 & 3.433 & 4.312 & 3.797 & 5.033 & 4.342 \\ MAE & 2.313 & 2.529 & 3.237 & 2.639 & 3.54 & 3.235 \\ \hline \multicolumn{8}{c}{PTMET} \\ \hline MSE & 141.62 & 100.222 & 80.541 & 88.762 & 122.58 & 86.393 \\ RMSE & 11.9 & 10.011 & 8.974 & 9.421 & 11.072 & 9.295 \\ MAE & 9.774 & 8.31 & 7.124 & 7.382 & 8.707 & 7.529 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of PTMRG and PTMET predictions per cylinder. performance for the PTMRG (using the RGCCV) than for the PTMET (using the exhaust gas temperature). The average MAE values for the PTMET and PTMRG across all cylinders were 8.138 and 2.916, respectively, showing a 64% improvement in MAE for the PTMRG compared to the PTMET. 5. Between the three metrics used to assess the maintenance time prediction algorithms, the MAE, which is less affected by outliers, is appropriate, considering the characteristics of engine data, such as wide normal operating temperature ranges, frequent fluctuations, and large temperature data ranges. In conclusion, by defining the necessary equations for predicting the maintenance time of engines and developing prediction algorithms, we established a foundation for implementing effective predictive maintenance for ship GEs through comparison and verification using machine learning. In future work, we plan to conduct comparative studies between the proposed maintenance time prediction algorithm and conventional time-based prediction methods. This will allow us to further validate the reliability and performance of our approach. Furthermore, we aim to develop an integrated predictive maintenance platform for ship engines, combining the abnormal symptom detection and maintenance time prediction algorithms proposed in this and previous studies. This platform will be applied to actual operating vessels and verified using real-time engine data, enabling us to evaluate its effectiveness and reliability in practical maritime environments.