# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 3 Results and Discussion
2.  
dition, due to temperature sensors, detection systems, and motor operation, abnormal data unrelated to motor abnormalities may be collected in real time. Therefore, the MAE, MSE, and RMSE, which were used here to evaluate the maintenance time prediction algorithms, are suitable for assessing such algorithms for ship engines' exhaust gas temperatures. Figures 8-12 show the performance comparison results and the calculated MSE, RMSE, and MAE of the prediction algorithms for cylinder nos. 1-5. Although the cylinders have different operating temperature ranges, as shown in the results for cylinder no. 6, Figures 8a, 9a, 10a, 11a and 12a for cylinder nos. 1-5 shows that the prediction data of the PTMRG-based algorithm follow the (collected) test data and are distributed near the test data. Figures 8b, 9b, 10b, 11b and 12b shows the PTMET results; the prediction data follow the test data but are widely and sporadically distributed compared with the PTMRG results. The MAE, MSE, and RMSE values also confirm that the PTMRG (using the RGCCV) has better prediction performance than the PTMET, which is based on the exhaust gas temperature. Table 8 is a summary of the prediction evaluation metrics for the PTMRG and PTMET for all cylinders. The average MAE values for the PTMET and PTMRG for cylinder nos. 1-6 are 8.138 and 2.916, respectively, indicating a 64% better MAE for the PTMRG than for the PTMET. As shown above, predicting the maintenance time using an indicator that reflects engine conditions under varying operational environments (PRMRG) is more practical and reliable than relying only on changes in cylinder exhaust gas temperature (PTMET). In a broader context, system-level prediction techniques have been widely studied in other engineering domains as well. For instance, [PERSON] et al. [26] proposed a hybrid FSO/RF communication model employing receive diversity under atmospheric turbulence, highlighting the effectiveness of analytical prediction approaches in dynamically changing environments. Similarly, the approach presented in this study--using real-time engine data for predictive maintenance--shares the same underlying goal of improving system reliability in the presence of fluctuating operating conditions. Efficient predictive maintenance can be con  
Figure 12: Performance comparison of **(a)** PTMRG-based and **(b)** PTMET-based maintenance time prediction algorithms for cylinder no. 5 of GE no. 2.  
ducted by identifying engine operation abnormalities under normal operating conditions using a previously developed engine data preprocessing and anomalous symptom detection algorithm [4], combined with the proposed maintenance time prediction algorithm. It should be noted that the engine model used in this study is a single model installed on a specific vessel. As such, the proposed algorithm is optimized for that particular engine configuration, and its applicability to other engine types remains an open question that requires further investigation. Nonetheless, the preprocessing framework and performance evaluation method based on engine operating conditions proposed in this study are expected to be applicable to a wide range of systems with further validation. Future research will focus on acquiring additional engine models to evaluate the generalizability and applicability of the proposed approach across various marine power systems and include comparative studies with classical prediction models to further validate the robustness of the proposed approach.