## 4 Example Verification
### Case Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Measuring Point**} & \multicolumn{1}{c}{**Rotational Speed (r/min)**} & \multicolumn{1}{c}{**Zone A Qualified**} & \multicolumn{1}{c}{**Zone B Level 1 Alarm (\(\upmu\)m)**} & \multicolumn{1}{c}{**Zone C Unit**} \\  & & **(\(\upmu\)m)** & & **Shutdown (\(\upmu\)m)** \\ \hline Swing of bearing & 79 & 0–260 & 260–530 & \textgreater{}530 \\ Vibration of oil receiver & 79 & 0–250 & 250–350 & \textgreater{}350 \\ and bearing & 79 & 0–350 & 350–600 & \textgreater{}600 \\ Vibration stator frame & 79 & 0–350 & 350–600 & \textgreater{}600 \\ Vibration of water & & & & \\ guide bearing and & 79 & 0–120 & 120–170 & \textgreater{}170 \\ combined bearing & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Vibration swing alarm threshold of monitoring system of hydropower station. Figure 12: The changing curve of correlation monitoring parameters of hydropower units in case 3: (**a**) vibration of oil receiver in the Y direction, (**b**) vibration of combined bearing in the Y direction, (**c**) vibration of water guide bearing in the Y direction, (**d**) temperature of forwarding thrust bearing bush, (**e**) temperature of the stator core. To reduce the influence of accidental factors and verify the universality of the proposed method, case 2 and case 3 are further used for analysis. It can also be seen from in case 2 and case 3 that when the unit fails, the amplitude of each monitoring parameter of the hydropower unit is far lower than the alarm threshold of the monitoring system. Although with the change of time, the vibration amplitude of the oil receiver in the Y direction fluctuates in the range of 20 to 50 \(\upmu\)m, and the amplitude value changes greatly, it is still far lower than 250 \(\upmu\)m. It can be seen from case 2 that the CMD curve exceeded the alarm threshold of 0.8 at 5:50 on 31 January 2018, and the fault status of the unit was monitored in advance. In case 3, the CMD curve also exceeded the alarm threshold of 0.8 at 20:20 on 11 December 2018, and the fault status of the unit was also monitored in advance. It can be seen from the above cases that the fault alarm and early warning means of the hydropower station monitoring system are relatively single, and the alarm setting value of the hydropower station monitoring system is mainly fixed value. However, in the process of equipment operation, with the continuous change of monitoring status with environmental conditions and operating conditions, the collected monitoring point data amplitude does not exceed the set value, but the equipment has failed. Therefore, the role of hydropower station monitoring system in engineering application is limited. Although the deterioration or failure information of hydropower units must be reflected in the massive data collected from each monitoring point, the above failures were found in the scheduled maintenance. This also shows that it is difficult for hydropower station staff to master the operation status of units in real-time through the information reflected by these massive data. In view of the problems existing in these engineering applications, this paper studies how to extract the operation status information from the massive monitoring data. According to the method proposed in this paper, the health model of hydropower units in normal operation is established, this model reflects the complex relationship between the operation status of hydropower units and multiple monitoring parameters, and monitoring index based on MD is set to measure the change degree of these complex links. From the above cases, the CMD curve is abnormal ahead of the maintenance time of the staff.