VMD and Attention Mechanism Based Bi-LSTM Model for Malfunction Identification of Optical Fiber Composite Submarine Cables

As the main electrical equipment of offshore power grids, optical fiber composite submarine cables undertake the task of power transmission and data communication. In order to ensure the proper functioning of the submarine cable, it is necessary to analyze the working state of it and identify and locate malfunction events. This paper proposes a malfunction identification model of submarine cables with VMD and attention based Bi-LSTM. First, we use the finite element analysis (FEA) and ANSYS software to generate the vibration waveforms of three main malfunction events of submarine cables. Then by generating the detection matrix of additive white gaussian noise (AWGN) and the vibration waveforms, it can realize the orientation and detection of malfunction events in single submarine cable. In addition, the vibration signal can be decomposed into IMF components using VMD for feature extraction. Moreover, the IMF components are input to the attention layer for feature fusion and Bi-LSTM module for further feature extraction. Finally, the result of malfunction identification is output through the classification layer. According to the comparative experiment and ablation experiment, the proposed method has proved to outperform the other benchmark models and is robust and stable under the condition of different signal-to-noise ratios (SNRs).