论文标题
使用深度转移学习和复发性神经网络对相位系统系统进行强度和相位堆叠分析
An Intensity and Phase Stacked Analysis of Phase-OTDR System using Deep Transfer Learning and Recurrent Neural Networks
论文作者
论文摘要
分布式声传感器（DAS）是有效的设备，在许多应用区域中广泛使用，用于记录各种事件的信号，这些事件沿光纤沿光纤沿着非常高的空间分辨率。为了正确地检测和识别记录的事件，具有高计算需求的高级信号处理算法至关重要。卷积神经网络是提取空间信息的高功能工具，非常适合DAS中的事件识别应用。长期术语内存（LSTM）是处理顺序数据的有效工具。在这项研究中，我们提出了一种多输入多输出的两个阶段提取方法，该方法将这些神经网络架构的能力与传递学习的能力结合在一起，以对压电传感器应用于光纤上的振动进行分类。首先，我们从相位-OTDR记录中提取了差分幅度和相位信息，并将它们存储在时间空间数据矩阵中。然后，我们在第一阶段使用了最先进的预训练的CNN作为特征提取器。在第二阶段，我们使用LSTMS进一步分析了CNN提取的特征。最后，我们使用密集层来对提取的特征进行分类。为了观察使用的CNN体系结构的效果，我们通过五个最先进的预训练模型（VGG-16，Resnet-50，Densenet-121，Mobilenet和Inception-V3）测试了模型。结果表明，在我们的框架中使用VGG-16体系结构可以在50个培训中获得100％的分类精度，并在我们的相位数据集中获得最佳结果。这项研究的结果表明，与LSTM结合的预训练的CNN非常适合分析差分振幅和相信息，在时间空间数据矩阵中表示，这对于DAS应用中的事件识别操作有希望。
Distributed acoustic sensors (DAS) are effective apparatus which are widely used in many application areas for recording signals of various events with very high spatial resolution along the optical fiber. To detect and recognize the recorded events properly, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks are highly capable tools for extracting spatial information and very suitable for event recognition applications in DAS. Long-short term memory (LSTM) is an effective instrument for processing sequential data. In this study, we proposed a multi-input multi-output, two stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning to classify vibrations applied to an optical fiber by a piezo transducer. First, we extracted the differential amplitude and phase information from the Phase-OTDR recordings and stored them in a temporal-spatial data matrix. Then, we used a state-of-the-art pre-trained CNN without dense layers as a feature extractor in the first stage. In the second stage, we used LSTMs to further analyze the features extracted by the CNN. Finally, we used a dense layer to classify the extracted features. To observe the effect of the utilized CNN architecture, we tested our model with five state-of-the art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet and Inception-v3). The results show that using the VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings and got the best results on our Phase-OTDR dataset. Outcomes of this study indicate that the pre-trained CNNs combined with LSTM are very suitable for the analysis of differential amplitude and phase information, represented in a temporal spatial data matrix which is promising for event recognition operations in DAS applications.
