The Analysis of Angle Resolution of Stress Vector Sensor Based on Optical Fiber Sensing Cable for High Speed Railway Traffic

With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress vector information is the important index to make more reasonable judgments about railway safety. However, information sensed by lots of commercial optical sensors is scalar. According to the stress filed distribution of rail, this paper proposes a new type of stress vector sensor based on optical fiber sensing cable(OFSC) with a symmetrical seven optical fibers structure and analyzes the relations between angle resolution and distance between adjacent of optical fibers through finite-element software(ANSYS) simulation. Through reasonable distance configuration, the angle resolution of the OFSC can be improved, and thus stress vector information, including the stress magnitude and the angle of stress, can be more accurately obtained. The simulation results are helpful to configure OFSC for angle resolution improvement in actual practice, and increase the safety factor in high speed railway structure health monitoring.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.

DFOS Applications to Geo-Engineering Monitoring

Optical fiber sensing technology has developed rapidly since the 1980s with the development of the optical fiber and fiber optical communication technology.It is a new type of sensing technology that uses light as a carrier and optical fiber as a medium to sense and transmit external signals(measurands).Distributed fiber optical sensors(DFOS)can continuously measure the external physical parameters distributed along the geometric path of the optical fiber.Meanwhile,the spatial distribution and change information of the measured physical parameters over time can be obtained.This technology has unmatched advantages over traditional point-wise and electrical measurement monitoring technologies.This paper summarizes the state-of-the-art research of the application of the distributed optical fiber sensing tech no logy in geo-engineering in the past 10 years,mainly including the advantages of DFOS,the challenges in geo-engineering monitoring,related fundamental theoretical issues,sensing performance of the optical sensing cables,distributed optical fiber monitoring system for geo-engineering,and applications of optical fiber sensing technology in geo-engineering.