# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 6 Identification of Monitoring Parameters in Line-Failure Scenarios  
In this section, the monitoring parameters in different line-failure locations were identified. Of course, installing more sensors would have provided better accuracy of failure monitoring; however,  
Figure 10: Time histories of the mooring tension at the middle location (top mooring line) under different wave conditions (**a**) and the corresponding spatial distribution along the net length at \(H_{s}\) = 5.0 m and \(T_{p}\) = 10.0 s (**b**) current velocity = 0.6 m/s. Figure 9: Time histories of the vertical motions of the net at the top-middle location (Y = 0 m, Z = \(-\)23 m) in different wave conditions (**a**) and motion envelopes along the net length at \(H_{s}\) = 5.0 m and \(T_{p}\) = 10.0 s (**b**) (current velocity = 0.6 m/s). considering the cost of the entire bottom-set gillnet, this was unfeasible. Therefore, finding key monitoring parameters was required. In this regard, the limited number of sensors were located in the net assembly, the location buoy, and mooring lines, as shown in Figure 2. In particular, an accelerometer was installed in the center location of the net assembly to measure accelerations in 3 DOFs, a global positioning system (GPS) was attached to a location buoy in the center to measure displacements in 3 DOFs, and tension sensors were positioned at the top mooring lines at the center and end locations of the net assembly. Since the GPS measures motions of the buoy, real-time wave statistics, e.g., significant wave height and peak period, could be acquired by solving the inverse problem using a Kalman filter, which has been done by other authors [34]. Authors have proven that the real-time inverse estimation of ocean waves from heave sensor signals using an adaptive Kalman-filter technique successfully reproduces the real-time wave elevations and spectra. The proposed physical system had 14 mooring lines, and critical parameters to detect failure scenarios were investigated. Additionally, a rope connecting the net assembly to a location buoy could be broken, and hence the effect of the rope failure was also checked. A GPS cannot deliver its signal in the water; thus, it is necessary for the buoy not to be fully submerged during operation. Besides, if the length of the rope is too short, it can cause significant vertical motions of the net by the heave motions of the location buoy. These considerations needed to be investigated before key-parameter identifications. Figure 11 shows the time histories of the dry length (or freeboard), which was defined as the instantaneous length of a buoy (volume = 50 liters) above the water surface at different rope lengths. An \(H_{\text{s}}\) and \(T_{p}\) of 2 m and 7 s, respectively, were applied. It was seen that the rope length significantly influenced the dry length of the location buoy. When the rope was short, the interaction between the net assembly and the location buoy was significant, i.e., the short rope could more easily pull down the location buoy. On the other hand, for a long slack rope, the interaction between the net and buoy became weaker, resulting in smaller variations of freeboard. Figure 12 shows the time histories of the vertical motion of the net assembly in the middle location. A case without location buoys was also simulated to check the importance of the interaction between the buoy and the net assembly. Due to the significant interaction between the net assembly and the location buoy, significant vertical motions occurred when the rope length was 50 m. However, the interaction effect became weaker when the rope length was increased. The vertical motions for the proposed design with 100 m rope length were similar to those without location buoys. In conclusion, the proposed location-buoy and rope designs with 100 m length were able to locate the GPS sensor with a view to minimizing buoy-net interactions and maintaining the buoy's freeboard.