• Convergence Security Journal
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• v.20 no.1
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• pp.59-68
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• 2020

The purpose of this study is to consider ways to stabilize the naval unit diagnosis system that has been implemented for five years. Check the historical process and theoretical background of the naval unit diagnosis system. This is to confirm the future direction of the naval unit diagnosis system research. Therefore, the importance of this system is confirmed and the direction of development is explained through application method. In particular, the study suggested the scientific development of analytical methods, the development of analytical programs, the development of leadership diagnostic programs, the increase of personnel in the unit diagnosis team, and the acquisition of expertise and reliability. In order for the naval unit diagnosis system to develop, internal and external continuous research is required.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.