Abstract

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 ($\Phi$-OTDR) optical fiber sensing system. Vibration signals were converted into ${128} \times {128}$ 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%.

© 2024 Optica Publishing Group

Highly discriminative and adaptive feature extraction method based on NMF–MFCC for event recognition of Φ-OTDR

Yi Huang, Jingyi Dai, Wei Shen, Xiaofeng Chen, Chengyong Hu, Chuanlu Deng, Lin Chen, Xiaobei Zhang, Wei Jin, Jianming Tang, and Tingyun Wang
Appl. Opt. 62(35) 9326-9333 (2023)

Intensity and phase stacked analysis of a Φ-OTDR system using deep transfer learning and recurrent neural networks

Ceyhun Efe Kayan, Kivilcim Yuksel Aldogan, and Abdurrahman Gumus
Appl. Opt. 62(7) 1753-1764 (2023)

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Yi Shi, Hanfang Liu, Wentao Zhang, Zhongdi Cheng, Jiewei Chen, and Qian Sun
Opt. Express 32(5) 8321-8334 (2024)

Perimeter monitoring of urban buried pipeline threated by construction activities based on distributed fiber optic sensing and real-time object detection

Suzhen Li, Renzhu Peng, Zelong Liu, and Xuqiang Liu
Opt. Express 32(2) 2590-2606 (2024)

AdaBoost-SCN algorithm for optical fiber vibration signal recognition

Hongquan Qu, Tingliang Feng, Yanping Wang, and Yuan Zhang
Appl. Opt. 58(21) 5612-5623 (2019)