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• W4288488818 endingPage "107247" @default.
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• W4288488818 abstract "The fishing net is the most important component of aquaculture net cages. Once damaged, it can cause substantial economic losses and ecological problems to the aquaculture industry. To avoid these subsequent issues caused by the damage to the fishing net, seeking a computerized, labor-saving approach and detecting damage in real time becomes the primary task of this paper. Inspired by recent development of the artificial neural networks and machine learning, this study proposes a fast and accurate approach for fishing net damage detection based on digital twin. Firstly, time-domain numerical simulations of the fishing net are conducted in a series of wave and current conditions to develop the artificial neural network-based digital twin. Then, the significant wave height, the spectral peak period, and tensions of vertical and horizontal ropes are used as input variables during the artificial neural network training; the intact and damaged states of the fishing net are considered outputs. Alongside this, the back-propagation learning algorithm is used for training to maximize damage detection performance. The results highlight that the digital twin model can effectively identify the fishing net damage using the sensor data, and the accuracy of the damage detection reaches above 93%. When the damage occurs at different net positions, the prediction accuracies of the artificial neural network model for training and testing sets are 93.97% and 93.00%, respectively. Regarding different wave-current directions, the prediction accuracies of the artificial neural network model are 99.87 % and 99.34 % for training and testing sets. Moreover, the developed digital twin can accurately detect the damage of the net even sea conditions and sensor data are not included in the training. The digital twin model proposed in the present study can potentially be used for damage detection in other aquaculture components and structures." @default.
• W4288488818 created "2022-07-29" @default.
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• W4288488818 date "2022-09-01" @default.
• W4288488818 modified "2023-09-28" @default.
• W4288488818 title "Digital twin for rapid damage detection of a fixed net panel in the sea" @default.
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• W4288488818 doi "https://doi.org/10.1016/j.compag.2022.107247" @default.
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