## 5 Experiment Validation
### Experiment Results  
In this section, we apply the Euler method in which the interval between PN and PA is very important for efficiently detecting changes in equipment status. A change point is a point where the mean and variance of classification accuracy change, which indicates that a change has occurred in the data. The change point can be quantitatively determined through change point analysis. It is possible, however, that the change point represents the beginning of equipment damage, but further analysis is needed to confirm this. To decide which are the best fitting parameters  
status as a result of this. The results are similar in Figures 9g-h and 10a-f. A characteristic of the evaluation measures is the wide fluctuations in _Precision_ and \(F1-score\) but the stability of accuracy values. It can be seen in Figure 10g,h that interesting changes have occurred in the evaluation measure values. After \(t\) is 50, there is a rapid change in equipment status, which was not evident before. In this step, the gearbox tooth is actually missing. It would have been impossible to prevent a catastrophic failure before \(t\) was 50 since changes in the evaluation measure were not identified until t was 50. At the point of catastrophic failure, \(d=1\) results in an evaluation measure less than 0.80. In the case of \(d=4\), the evaluation measure has a value of 1.0 and shows two distinct peaks. A constant interval (\(d\)) between PN and PA makes it possible to detect meaningful changes within that interval rather than cumulative changes. The \(d\) value of the abnormality monitoring system will depend on its design purpose. A large \(d\) value makes meaningful interpretation difficult because the comparison is with a point in time that is far from the present. Alternatively, if the \(d\) value is too small, it is difficult to capture equipment changes sensitively. The optimal \(d\) value for equipment monitoring remains a future task. Table 2 shows the overall results. A large difference between the average value of accuracy before and after the change point means the change in equipment status is large. The difference (\(D_{cp}\)) in average accuracy before and after the change point for each step can be seen in Table 2. The \(D_{cp}\) value was larger when \(d=4\) than when \(d=1\), which can be interpreted as more sensitive detection of abnormal signals when \(d=4\). When the alarm level was set to 0.80, an alarm occurred at Steps 1, 2, 3, and 9 for \(d=1\) and at Steps 1, 2, 3, 4, and 9 for \(d=4\). When the warning level was set to 0.95, a warning did not appear in any step for \(d=1\), but a warning occurred in Steps 1, 2, and 9 when \(d=4\). In conclusion, we know that when \(d=4\), abnormal signals were detected faster and more sensitively than when \(d=1\). It is necessary to conduct a complementary analysis rather than find the best \(d\) value because the characteristics of the information differ depending on the \(d\) value. Depending on the \(d\) value, a change in a narrow area may be sensitive to changes in a wider area, whereas a change in a broader area may be sensitive to changes in a narrow area. As a result, selecting \(d\) as a large value will enable you to see the trend of the overall change. By selecting a small value for \(d\), you will see the trend focusing on the current change rather than the accumulated change. By using \(d=4\), we were able to obtain more useful alarm and warning information in this paper. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Case** & **Interval Parameter (\(d\))** & **Step 1** & **Step 2** & **Step 3** & **Step 4** & **Step 6** & **Step 7** & **Step 8** & **Step 9** \\ \hline \multirow{3}{*}{Change Point (\(C_{p}\))} & \(d=1\) & 23 & 11 & 52 & 2 & 37 & 53 & NA & 51 \\  & \(d=4\) & 20 & 8 & 17 & 76 & 45 & 95 & 95 & 50 \\ \hline \multirow{3}{*}{Acc.