## 3 Results
Figure 9 shows the RMCCV and MCCV using the data average calculated based on the same ME ORDER RPM and VOYAGE to reduce the impact of noise data and improve the detection of anomalies. When the average values of the data are applied, the trends according to the voyage are similar for the MCCV and RMCCV (Figure 8), where all data are applied. However, the noise data of the RMCCV are confirmed to be reduced. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **MCCV** & **RMCCV** \\ \hline Mean & 175.0 & 175.0 \\ Std. & 11.6 & 6.1 \\ Min. & 142.2 & 146.5 \\ Max & 211.5 & 223.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the MCCV and RMCCV analysis data of cylinder 1 for all normal operating data (source: own elaboration). Figure 8: Comparison of MCCV and RMCCV of normal operating data of cylinder 1. (**a**) MCCV and (**b**) RMCCV (source: own elaboration, Python 3.8.3). Table 15 shows the MCCV and RMCCV data in the normal operating state of the ME ORDER RPM and VOYAGE standard average values. The MCCV and RMCCV averages were lower than the overall results presented in Table 13. The data distribution range was also narrowed to 45.6 for the MCCV and 22.1 for the RMCCV, which was reduced by about 70%. As for the standard deviation, the MCCV increased by 0.8 compared to the total data. However, by reducing the RMCCV by 2.3, it was possible to reduce the range of normal operating conditions and the impact of noise data by utilizing the average data based on ME ORDER RPM and VOYAGE. Based on the RMCCV of the mean values of ME ORDER RPM and VOYAGE for determining anomalistic symptoms, the concern level was set to 187-197, and the abnormal level was set to a section exceeding 197. Figure 10 shows the RMCCV results of all data, including voyage 21123 data with anomalies. The RMCCV of voyage 21123 increases over time. However, clearly determining anomalistic symptoms is difficult due to the noise data. Figure 11 shows the comparison results of the RMCCV of the average value data based on ME ORDER RPM and VOYAGE. Identifying anomalistic symptom trends with the MCCV in Figure 11a is difficult. In addition, the data of the concern level were included in the normal range, making it impossible to detect anomalistic symptoms. The RMCCV, as shown in Figure 11b, can detect anomalistic symptoms by confirming that the data of voyage 21123 rose beyond the normal data group to the concern level. \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **MCCV** & **RMCCV** \\ \hline Mean & 174.1 & 172.3 \\ Std. & 12.4 & 3.8 \\ Min. & 153.2 & 164.0 \\ Max & 198.8 & 186.1 \\ \hline \end{tabular}
\end{table} 
Table 15: Comparison of MCCV and RMCCV using the average normal operation data based on ME ORDER RPM and VOYAGE of cylinder 1 (source: own elaboration). Figure 9: Comparison of MCCV and RMCCV using the average of normal operating data based on ME ORDER RPM and VOYAGE of cylinder 1. (**a**) MCCV (**b**) RMCCV (source: own elaboration, Python 3.8.3). The verification results of the RMCCV algorithm using the average data of each VOYAGE were obtained to improve the detection of anomalistic symptoms. Table 16 presents the RMCCV results of the average normal operating data based on VOYAGE. The mean, standard deviation, and data distribution range improved compared to the average data analysis results based on ME ORDER RPM and VOYAGE. The concern level was set to 176-186, and the abnormal level was set to a section exceeding 186 based on the RMCCV of the VOYAGE averaged data for determining anomalistic symptoms. Figure 12 shows the results of anomalistic symptom detection using the average data based on the VOYAGE. The MCCV was unable to detect anomalies, as indicated by the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **MCCV** & **RMCCV** \\ \hline Mean & 173 & 172.2 \\ Std. & 11.2 & 2.2 \\ Min. & 160.5 & 169.3 \\ Max & 190.4 & 175.4 \\ \hline \hline \end{tabular}
\end{table}