# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 3 Results and Discussion
As shown in Table 7, the maintenance points predicted for all cylinders using PTROPET are, on average, 35.3 h later (by approximately 24%) than those obtained using PTROPRG. The PTMRG (PTRORG) is the time predicted using values adjusted for the operation condition based on the GE operating environment using the RGCCV, reflecting the current operating status of each cylinder. This is more practical and reliable than predicting the maintenance time using changes in the exhaust gas temperature of the cylinder, which is the criterion suggested by engine manufacturers for maintenance decisions [3,4]. The prediction performance of the maintenance time prediction algorithms based on the RGCCV and the exhaust gas temperature was compared, as shown in Figures 7-12. The predicted maintenance times for each cylinder were graphed by setting 75% (25%) of the 21,887 data points of VOYAGE 21113 as training (test) data. Then, the evaluation metrics (MSE, RMSE, and MAE) were calculated. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item\textbackslash{}Cylinder** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline MCET & 530 & 530 & 540 & 530 & 530 & 550 \\ PIET & \(-467.35\) & \(-400.17\) & \(-499.28\) & \(-490.35\) & \(-597.72\) & \(-586.33\) \\ PSET & \(10.9\times 10^{-5}\) & \(10.1\times 10^{-5}\) & \(11.5\times 10^{-5}\) & \(11.1\times 10^{-5}\) & \(12.5\times 10^{-5}\) & \(12.6\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation factors for maintenance time predicted using exhaust gas temperature (PTMET). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item\textbackslash{}Cylinder** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline PTMRG (h) & 2497.7 & 2523.5 & 2477.7 & 2509.2 & 2478.3 & 2465.9 \\ PTROPRG (h) & 150.0 & 175.9 & 130.0 & 161.6 & 130.7 & 118.2 \\ PTMET (h) & 2537.2 & 2562.5 & 2512.0 & 2543.8 & 2510.4 & 2498.3 \\ PTROPET (h) & 189.6 & 214.8 & 164.4 & 196.1 & 162.8 & 150.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction of maintenance time of all cylinders of GE no. 2. Figure 7: Performance comparison of **(a)** PTMRG-based and **(b)** PTMET-based maintenance time prediction algorithms for cylinder no. 6 of GE no. 2. Figure 7 shows the results for cylinder no. 6. Figure 7a shows the results of the PTMRG-based algorithm, where the predicted data follow the (collected) test data and are distributed close to the test data. By contrast, the PTMET results in Figure 7b show that the predicted data follow the test data but are widely and sporadically distributed relative to the PTMRG results, with large differences between the predicted and test data within certain maximum and minimum temperature ranges in the test data. The PTMRG- and PTMET-based algorithms have MAE values of 2.313 and 9.774, MSE values of 10.358 and 10.358, respectively. Figure 8: Performance comparison of (**a**) PTMRG-based and (**b**) PTMET-based maintenance time prediction algorithms for cylinder no. 1 of GE no. 2. Figure 9: Performance comparison of (**a**) PTMRG-based and (**b**) PTMET-based maintenance time prediction algorithms for cylinder no. 2 of GE no. 2. 141.62, and RMSE values of 3.218 and 11.9, respectively. The prediction evaluation metrics for the PTMET are relatively poor, confirming that the PTMRG, which uses the RGCCV, has better prediction performance. Depending on the engine specification and manufacturer, the cylinder exhaust gas temperature fluctuates widely between 250 and 500 \({}^{\circ}\)C during normal operation with load variations; temperature changes of approximately 10 \({}^{\circ}\)C or more may not be problematic, depending on the operating environment, and the scale of temperature data is large. In ad  
Figure 11: Performance comparison of (**a**) PTMRG-based and (**b**) PTMET-based maintenance time prediction algorithms for cylinder no. 4 of GE no. 2. Figure 10: Performance comparison of (**a**) PTMRG-based and (**b**) PTMET-based maintenance time prediction algorithms for cylinder no. 3 of GE no.