Du Yuzhou, Xu Banglian, Zhang Leihong, Zhang Yiqiang
Appl Opt. 2024 Mar 10;63(8):2011-2019. doi: 10.1364/AO.505020.
In the field of optical fiber vibration signal recognition, one-dimensional signals have few features. People often used the shallow layer of a one-dimensional convolutional neural network (1D-CNN), which results in fewer features being learned by the network, leading to a poor recognition rate. There are also many complex algorithms and data processing methods, which make the whole signal recognition process more complicated. Therefore, an optical vibration signal recognition method based on an efficient multidimensional feature extraction network was proposed. Based on ResNet-50, efficient channel attention (ECA) was used to improve image features extraction ability, and a long short-term memory (LSTM) network was used to enhance the extraction of temporal features. Three different vibration signals were collected using a phase-sensitive optical time-domain reflectometry (-OTDR) optical fiber sensing system. Vibration signals were converted into 128×128 grayscale images, which have more effective vibration information. The experimental results show that the three types of signals can be recognized and classified effectively by the network, and the average recognition rate is 98.67%.
在光纤振动信号识别领域，一维信号的特征较少。人们通常使用一维卷积神经网络（1D-CNN）的浅层，这导致网络学习到的特征较少，从而导致识别率较低。此外，还有许多复杂的算法和数据处理方法，这使得整个信号识别过程更加复杂。因此，提出了一种基于高效多维特征提取网络的光纤振动信号识别方法。基于ResNet-50，使用高效通道注意力（ECA）来提高图像特征提取能力，并使用长短期记忆（LSTM）网络来增强时间特征的提取。使用相敏光时域反射仪（-OTDR）光纤传感系统采集了三种不同的振动信号。将振动信号转换为128×128的灰度图像，其中包含更有效的振动信息。实验结果表明，该网络能够有效地对三种类型的信号进行识别和分类，平均识别率为98.67%。
