Abstract:If damages to aquaculture nets are not found in time, they will result in the escape of fish, thereby, causing considerable losses to farmers. Therefore, it is necessary to detect whether damage to fishing net occurs. At present, the primary method for detecting damage to fishing nets is the manual inspection of staff diving into the water, but this method is labor-intensive and inefficient. This paper proposes a damage detection method based on digital twin, which uses sensor monitoring instead of manual monitoring to overcome these limitations and realize real-time monitoring of fishing nets. The research first shows that the numerical simulation data in good agreement with the physical model experimental data can be obtained through the numerical simulation of the lumped mass mechanical model. In the numerical simulation, considering a kind of damage to the fishing net, a total of 11 simulations were carried out: the tensile values of the horizontal and vertical ropes of the fishing net, nine sea conditions as training samples, and two sea conditions as test samples. The artificial neural network adopts the error backpropagation training method that takes the significant wave height Hs, the spectral peak period Tp, and the tensile value of the vertical and horizontal rope as inputs, and the complete state and damaged state of the fishing net as the outputs. After training, the recognition model recognition accuracy rates for the training and test samples were 99.21% and 95.11%, respectively. The measured actual physical sensor data were also used as test data. The recognition accuracy of the recognition model is 94.32%, which indicates the feasibility of the practical application of digital twin technology in the damage detection of the net. It can, therefore, be used as a new method of fishing net damage detection. As the wave-current environment is more complex in the actual sea area, our following research will focus on dealing with the sensing data and detection of the damage of fishing nets in a more realistic sea condition.
