## 3 Results

Table 16: Comparison of MCCV and RMCCV data of average normal operating conditions based on VOYAGE of cylinder 1 (source: own elaboration). Figure 10: RMCCV including anomaly symptom data of cylinder 1 (source: own elaboration, Python 3.8.3). results presented in Figures 6 and 11. However, the RMCCV could detect anomalistic symptoms in the engine more clearly than the results using the mean data based on ME ORDER RPM, VOYAGE, and all data. Table 17 summarizes the MCCV correction factor analysis results and revised coefficients of each cylinder for calculating the RMCCVs of the cylinders. \(\rm R^{2}_{adj}\) between CET and the correction factor showed a high explanatory power of 0.754-0.857, depending on the cylinder. Moreover, \(\rm R^{2}_{adj}\) between the CCP and the correction factor showed a very high explanatory power as the range of 0.952-0.968. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cylinder** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multicolumn{8}{c}{Cylinder exhaust temperature} \\ \hline R\({}^{2}\) & 0.795 & 0.867 & 0.793 & 0.880 & 0.845 & 0.869 \\ Adj. R\({}^{2}\). & 0.757 & 0.842 & 0.754 & 0.857 & 0.817 & 0.844 \\  & Load & 0.03 & 0.014 & 0.260 & 0.555 & 0.234 & 0.001 \\ \(p>|\)t\(|\) & Scav. temp, & 0.001 & 0.000 & 0.001 & 0.000 & 0.000 & 0.000 \\  & Scav. press. & 0.053 & 0.127 & 0.259 & 0.581 & 0.220 & 0.013 \\  & MAE & 4.685 & 5.541 & 7.411 & 4.946 & 5.332 & 4.095 \\ \hline \multicolumn{8}{c}{Regression (revision) coefficient} \\ \hline ELR & 1.8184 & 2.1837 & 1.2959 & 0.4746 & 1.1577 & 2.7299 \\ ESTR & 0.9095 & 1.5505 & 1.3895 & 1.5084 & 1.4077 & 1.1866 \\ ESPR & \(-27.9974\) & \(-22.3134\) & \(-22.8538\) & \(-7.8111\) & \(-21.0387\) & \(-32.8586\) \\ \hline \multicolumn{8}{c}{Cylinder MAX combustion pressure} \\ \hline R\({}^{2}\) & 0.970 & 0.961 & 0.955 & 0.959 & 0.964 & 0.966 \\ Adj. R\({}^{2}\). & 0.968 & 0.959 & 0.952 & 0.957 & 0.962 & 0.965 \\ \(p>|\)t\(|\) Load & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ MAE & 1.375 & 1.721 & 1.867 & 1.688 & 1.756 & 1.188 \\ \hline \multicolumn{8}{c}{Regression (revision) coefficient} \\ \hline CPLR & 3.3900 & 3.3459 & 3.1987 & 3.3506 & 3.4613 & 3.3541 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Revision factors and results of all cylinder MCCV correction factors for the abnormal symptom prediction of the propulsion engine (source: own elaboration). Figure 12: RMCCV of average data including anomaly symptoms based on VOYAGE of cylinder 1. (**a**) MCCV and (**b**) RMCCV (source: own elaboration, Python 3.8.3). The \(p\)-values between the CET and the engine load and scavenging air pressure correction factors are relatively high for cylinders 3, 4, and 5. This is due to the high correlation between the engine load and scavenging air pressure. \(\rm R^{2}_{adj}\) and \(p\)-value are indicators that identify the relation between the explanatory and response variables [19; 37]. Therefore, the reference data used for determining anomalistic symptoms affected the \(p\)-value owing to the use of a database with a limited data range of \(\geq\)135 RPM based on ME ORDER RPM [19; 37]. The MAE of the CET according to the correction factor was in the range of 4.095-7.411, exhibiting good prediction performance compared to the exhaust gas temperature scale. The correction coefficients (ELR, ESTR, and ESPR) of each CET were effectively derived considering \(\rm R^{2}_{adj}\), \(p\)-value, and MAE. The MAE range of the entire CCP was 1.188-1.867, and the prediction result was highly reliable. In addition, considering \(\rm R^{2}_{adj}\) and the \(p\)-value, the CCP correction factor (CPLR) was highly reliable, and a valid RMCCV was calculated. Table 18 presents the RMCCV results of each cylinder using the average data based on ORDER RPM and VOYAGE. Table 19 lists the RMCCV results of each cylinder using the average data based on VOYAGE.