## 3 Results
However, determining anomalistic symptoms in real time using all voyage data has limitations, necessitating improved algorithm reliability through reduced algorithm processing time and minimized noise data. Therefore, the average values of data with a frequency count of \(>\)1000 EA based on ME ORDER RPM and VOYAGE were derived (Table 11) and set as the steady state reference data. Furthermore, data analysis and correction were performed to set the normal range of consistent MCCVs according to factor changes. The obtained results of the regression analysis of the CET and maximum CCP, which are the components of the MCCV and the selected MCCV correction factor, are as follows. Figure 7a shows the results of the regression analysis of the exhaust gas temperature and MCCV correction factor of cylinder 1. The engine load (ME LOAD), scavenging air temperature (ME SCAV. AIR RECEIVER TEMP. ), and scavenging air pressure (ME SCAV. AIR RECEIVER IN PRESS.) were determined as correction factors considering the explanatory power with CET and the influence of engine combustion. The R\({}^{2}\)adj of CET and three correction factors was 0.757, showing significant explanatory power. In addition, the \(p\)-value (\(p>|\mathfrak{t}|\)), which is used to evaluate the effect of each correction factor on the CET, is <0.05 for all three correction factors. In addition, MAE, which is a predictive index of CET according to the correction factor, was 4.685, and the selected correction factor was found to be significant. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ME Order** & **Voyage** & **RPM** & **Load** & **Scav. Air** & **Scav. Air** & **Exh. Gas** & **Cyl. Max. ** & **Cyl. Comp. ** \\
**RPM** & & & **(\%)** & **Temp. ** & **Press. ** & **Temp. ** & **Press. ** & **Press. ** \\
**136.1** & 21081 & 136.0 & 72.7 & 44.5 & 2.1 & 350.2 & 163.2 & 119.6 \\
**136.8** & 21091 & 136.7 & 74.1 & 43.3 & 2.2 & 347.1 & 168.8 & 126. Figure 7b shows the results of the regression analysis of the maximum combustion pressure of cylinder No. 1 and the MCCV correction factor. Based on the analysis of the relationship with engine combustion and the explanatory power with the maximum CCP, \(\text{R}^{2}\text{adj}\) showed a very high explanatory power of 0.968 only with the engine load (ME LOAD). In addition, the \(p\)-value (\(p>|t|\)) was analyzed as a significant factor at 0.00, and the engine load was selected as a correction factor for the maximum CCP. Also, the MAE of the CCP with the engine load was 1.375, showing excellent predictive performance. The correction standard for calculating a constant MCCV in response to changes in factors such as engine load had the highest data frequency count of 13,470 EA among the classified data presented in Table  
Using the MCCV correction factor regression coefficient, the correction criteria, and Equation (5), an RMCCV based anomalistic symptom judgment algorithm was constructed and verified as follows. The results of the MCCV and RMCCV based on all normal operation status data of cylinder 1 are shown in Figure 8.  
The MCCV showed a decreasing trend as the voyage progressed according to changes in the engine operating environment, such as target RPM. However, the RMCCV was corrected to respond to changes in engine operating conditions, and it remained relatively constant as the voyage progressed. However, some RMCCV data were overcorrected, resulting in abnormalities in noise data and the main data group, which are the criteria for determining the anomalistic symptoms of the MCCV. Table 14 shows the MCCV and RMCCV results during normal operation. The mean value is the same, whereas the standard deviation improved to 11.6 for the MCCV and 6.1 for the RMCCV. However, the distribution range of the data was from 142.2 to 211.5 (69.3) in the case of the MCCV and from 146.5 to 223.3 (76.8) for the RMCCV owing to the noise data caused by overcorrection.