Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing

High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.

High Resolution Distributed Optical Fiber Sensing Using Time-Expanded Phase-Sensitive Reflectometry

We have demonstrated a novel scheme for distributed optical fiber sensing based on the use of a dual frequency comb, which enables the development of a high-resolution (in the cm range) distributed sensor with significantly relaxed electronic requirements compared with previous schemes. This approach offers a promising solution for real time structure monitoring in a variety of fields, including transportation, manufacturing or mechatronics. In this work, we review the principle of operation of the technique, recent advances to improve its performance and different experimental tests.

Multi-gas leakage synchronous monitoring system based on the distributed optical fiber sensing technology

In the process of long-distance transportation of different gas, the remote pipeline plays an irreplaceable role in energy transmission. When the pipeline is laid in remote areas for a long distance, it is easy to be influenced by geological disasters and complex working conditions, which may lead to corrosion and leakage. therefore, it is necessary to conduct pipeline gas real-time safety monitoring. An optical fiber gas leakage synchronous monitoring system was proposed and demonstrated based on distributed optical sensing technology for simultaneous multi-gas measurements. In this study, we discuss that the principle of multi-gas leakage synchronous monitoring system is investigated and then validated by the theoretical simulation experiments. Furthermore, gas concentration and leakage location discrimination tests are also conducted in laboratory. The experimental results show that the output intensity values increased obviously along with the gas concentration changes, and the response time of the sensor system is about 40 seconds, and it’s concluded that the multi-gas leakage synchronous monitoring system based on distributed optical fiber sensing technology exhibited good sensing and location discrimination performance.