基于小波包与神经网络的海底电缆故障诊断

A Fault Diagnosis Method Forwavelet Packet and Neural Network-Based Submarine Cables

针对海底电缆的故障诊断问题,提出了一种基于小波包与神经网络的诊断新方法。该方法将海缆中光纤的布里渊频移转换成温度和应变数据,首先使用六层小波分解对监测信号降噪,然后对故障数据进行三层小波包分解和重构,再通过提取能量、标准差和Shannon熵等构造特征向量并输入BP(反向传播)神经网络进行训练和测试。实验分析表明,在海底电缆故障诊断中,采用标准差作为特征向量输入BP神经网络中的诊断方法性能最佳。

A new diagnosis method based on wavelet packet and neural network is proposed for the fault diagnosis of submarine cables.By this method,the fiber Brillouin frequency shifts in the cables are converted into temperature and strain data.The monitoring signals are first denoised by using the six-layer wavelet decomposition and the faulty data are decomposed and reconstructed by three-layer wavelet packet,and then such structural eigenvectors as energy,standard deviation and Shannon entropy are extracted and inputted to the Back Propagation(BP)neural network for training and testing.The experimental analysis shows that inputting the standard deviation as the eigenvector to the BP neural network is the optimum method for the fault diagnosis in submarine cables.