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• A fast and accurate fishing net damage detection model has been established based on digital twin. • Different damaged levels, positions and wave-current directions have been tested. • The digital twin model can accurately identify whether the fishing net is damaged or not. 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. [ABSTRACT FROM AUTHOR]

This paper presents a method for detecting damage of a bottom-set gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. In this regard, time-domain numerical simulations for a 300-m-long bottom-set gillnet with an equivalent-drag-net model are extensively performed. Various wave conditions as well as numerous damaged scenarios are considered in the numerical simulations, and extensive data are collected for the training and testing of the ANN-based machine-learning scheme. In training, representative sea states, net-assembly accelerations, and location-buoy displacements are selected as the input variables. The back-propagation learning algorithm is employed for training to maximize the damage-detection performance. The output of the ANN model is the identification of the type and location of the damaged net. The damage-detection capability is significantly enhanced by employing the moving standard deviation and median filter. The well-trained ANN models are shown to accurately (96% correct) detect the damage of the net even for the sea states not included in training. This study demonstrates that the real-time automatic monitoring of the underwater net systems can be designed by using the digital-twins technology with the applied ANN model and machine-learning scheme for the detection of damages and malfunctions. • The well-trained ANN models are shown to accurately (96% correct) detect the damage of the fishing net. • The number of neurons, median filter, and moving standard deviation can improve the performance of the ANN model. • The sensor-based fishing-net monitoring system can be designed by digital-twins technology with the applied ANN model. [ABSTRACT FROM AUTHOR]