Abstract
With the continuous growth of the global submarine cable laying length, the frequency of faults and the caused economic losses are increasing year by year. Rapid identification of submarine cable faults can reduce economic losses. For several typical submarine cable faults, this paper proposes a recognition method based on FCN-GRU-SVM. First, pre-process the original signal, then import the data into FCN-GRU to extract the signal features, and finally use SVM to identify the fault types. The experimental verification using the data set obtained by finite element simulation shows that the proposed method is superior to the comparison model and other time-frequency processing methods in accuracy and robustness.
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