# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 6 Identification of Monitoring Parameters in Line-Failure Scenarios
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mooring-Line-Breakage**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**DX**} & \multicolumn{2}{c}{**AX**} & \multicolumn{2}{c}{**AY**} \\  & **Location** & & **(Y = 0 m)** & **(Y = \(-\)150 m)** & **(Y = 0 m)** & **(Y = 0 m)** & **(Y = 0 m)** & **(Y = 0 m)** \\ \hline Y & Z & AVE & STD & AVE & STD & AVE & STD & STD & STD \\ Location & Location & (kN) & (kN) & (kN) & (kN) & (m) & (m) & (m/s\({}^{2}\)) & (m/s\({}^{2}\)) \\ No breakage & 0.138 & 0.023 & 0.084 & 0.016 & 96.330 & 0.106 & 0.033 & 0.000 \\
0 m & TOP & 0.000 & 0.002 & 0.084 & 0.016 & 96.579 & 0.105 & 0.135 & 0.000 \\
0 m & BOT & 0.231 & 0.025 & 0.084 & 0.016 & 96.341 & 0.105 & 0.036 & 0.000 \\
0 m & TOP and BOT & 0.000 & 0.002 & 0.009 & 0.016 & 113.370 & 1.690 & 0.232 & 0.000 \\ \(-\)50 m & TOP & 0.138 & 0.022 & 0.084 & 0.016 & 96.327 & 0.105 & 0.067 & 0.027 \\ \(-\)50 m & BOT & 0.138 & 0.022 & 0.084 & 0.016 & 96.327 & 0.105 & 0.043 & 0.010 \\ \(-\)50 m & TOP and BOT & 0.187 & 0.026 & 0.094 & 0.016 & 96.309 & 0.105 & 0.050 & 0.018 \\ \(-\)100 m & TOP & 0.138 & 0.022 & 0.085 & 0.015 & 96.326 & 0.105 & 0.035 & 0.020 \\ \(-\)100 m & BOT & 0.138 & 0.022 & 0.085 & 0.015 & 96.327 & 0.105 & 0.034 & 0.008 \\ \(-\)100 m & TOP and BOT & 0.140 & 0.023 & 0.163 & 0.024 & 96.325 & 0.106 & 0.034 & 0.024 \\ \(-\)150 m & TOP & 0.138 & 0.023 & 0.000 & 0.001 & 96.326 & 0.105 & 0.032 & 0.029 \\ \(-\)150 m & BOT & 0.138 & 0.022 & 0.138 & 0.019 & 96.327 & 0.105 & 0.032 & 0.006 \\ \(-\)150 m & TOP and BOT & 0.133 & 0.022 & 0.000 & 0.001 & 96.314 & 0.105 & 0.035 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical data of mooring tension, surge motion of the location buoy, and acceleration of the net assembly in the X and Y directions in different mooring-line-breakage scenarios at \(H_{s}\) = 1.0 m, \(T_{p}\) = 6.0 s, and current velocity = 0.6 m/s (AVE = average value and STD = standard deviation). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mooring-Line-Breakage**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**DX**} & \multicolumn{2}{c}{**AX**} & \multicolumn{2}{c}{**AY**} \\  & **Location** & & **(Y = 0 m)** & **(Y = \(-\)150 m)** & **(Y = 0 m)** & **(Y = 0 m)** & **(Y = 0 m)** \\ \hline Y & Z & AVE & STD & AVE & STD & AVE & STD & STD & STD \\ Location & Location & (kN) & (kN) & (kN) & (kN) & (m) & (m) & (m/s\({}^{2}\)) & (m/s\({}^{2}\)) \\ No breakage & 0.152 & 0.066 & 0.096 & 0.046 & 96.713 & 0.217 & 0.070 & 0.000 \\
0 m & TOP & 0.000 & 0.003 & 0.096 & 0.045 & 96.946 & 0.216 & 0.264 & 0.000 \\
0 m & BOT & 0.247 & 0.086 & 0.096 & 0.045 & 96.714 & 0.211 & 0.102 & 0.000 \\
0 m & TOP and BOT & 0.000 & 0.003 & 0.101 & 0.045 & 114.118 & 1.680 & 0.397 & 0.000 \\ \(-\)50 m & TOP & 0.150 & 0.061 & 0.096 & 0.045 & 96.700 & 0.210 & 0.082 & 0.042 \\ \(-\)50 m & BOT & 0.151 & 0.062 & 0.096 & 0.045 & 96.700 & 0.210 & 0.078 & 0.017 \\ \(-\)50 m & TOP and BOT & 0.200 & 0.074 & 0.105 & 0.045 & 96.671 & 0.210 & 0.089 & 0.042 \\ \(-\)100 m & TOP & 0.151 & 0.063 & 0.096 & 0.043 & 96.700 & 0.210 & 0.074 & 0.033 \\ \(-\)100 m & BOT & 0.150 & 0.062 & 0.096 & 0.044 & 96.700 & 0.210 & 0.071 & 0.016 \\ \(-\)100 m & TOP and BFinally, Figure 18 shows the time history of the surge motion of the location buoy in the middle location as the connecting rope is broken after 600 s. As is clearly shown in the figure, the surge motion increased continuously due to free drifting on the sea surface. In this case, there should be little changes to other monitoring parameters, simply resulting in the loss of the location buoy, which can easily be fixed. When the whole net is drifting with buoys, fishermen can easily check the location of the gillnet in real time through GPS and collect it. Through this kind of computer-simulation tool, a useful big-data set can be generated and machine-based problem-detection or decision-making processes can be established using real-time sensor signals.