Distributed Acoustic Sensing (DAS) for Intelligent In-Motion Transportation Condition Monitoring

多物理 计算机科学 无损检测 状态监测 振动 异常检测 磁道(磁盘驱动器) 实时计算 智能交通系统 软件 模拟 工程类 声学 有限元法 人工智能 电气工程 运输工程 结构工程 医学 操作系统 物理 放射科 程序设计语言
作者
Hossein Taheri,Michael Jones,Suyen Bueso Quan,Maria Gonzalez Bocanegra,Mohammad Taheri
标识
DOI:10.1115/imece2022-95366
摘要

Abstract Safety is the top priority for every transportation system. Although various aspects of transportation infrastructure’s safety have been studied, in-motion monitoring and detection of defect is still a big concern. Understanding the trend of anomalies, and how to monitor undesired conditions are of high interest in transportation. In this study, the technology of Distributed Acoustic Sensing (DAS) for in-motion rail condition monitoring is studied through experimental testing and simulation modeling. DAS uses fiber optic cables along the track to detect any anomaly indicator. DAS permit the measurement of a desired parameter as a function of length along the fiber. Despite any conventional Nondestructive Testing (NDT) technique where the coverage or scanning area of the sensors are very limited, DAS provides a full, fast and accurate coverage of all section under the test. The objective of this research is to provide an assessment of anomaly detection and monitoring techniques based on DAS for transportation investigation. It presents the experimental evaluations and numerical simulations on the current methodologies in DAS systems. DAS was used to evaluate the transportation traffic condition in a rural area by connecting an available underground dark fiber to the DAS interrogator and system as well as simulated traffic condition in smaller scale in a parking lot. COMSOL Multiphysics software was used to model the interaction of ambient vibration with the fiber optic. Results show that the condition of the transportation can be monitored and detected by DAS with an appropriate accuracy. DAS information can be used for traffic condition monitoring, object tracking and flaw detections in the transportation lines.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
田様应助日月昭采纳,获得10
刚刚
1秒前
issxxo完成签到,获得积分10
2秒前
布噜布噜发布了新的文献求助10
2秒前
kaka发布了新的文献求助10
2秒前
2秒前
辛夷完成签到 ,获得积分10
3秒前
3秒前
西吴完成签到 ,获得积分0
4秒前
6秒前
6秒前
6秒前
完美世界应助莫等闲采纳,获得10
6秒前
dddd完成签到,获得积分10
6秒前
帅帅哥完成签到,获得积分10
8秒前
走走走发布了新的文献求助20
8秒前
8秒前
8秒前
worywang完成签到,获得积分10
9秒前
多多发布了新的文献求助10
9秒前
乐乐应助cuicuisha采纳,获得10
9秒前
maomao完成签到 ,获得积分10
9秒前
simon发布了新的文献求助10
10秒前
10秒前
单身的海白完成签到,获得积分10
11秒前
11秒前
大胆日记本完成签到 ,获得积分10
11秒前
dddd发布了新的文献求助10
11秒前
大个应助Bella_qcx采纳,获得10
12秒前
12秒前
13秒前
蒋大饼完成签到,获得积分10
13秒前
Owen应助桃子采纳,获得10
14秒前
南望完成签到,获得积分10
14秒前
Hbobo完成签到,获得积分10
14秒前
15秒前
JamesPei应助玖东采纳,获得10
16秒前
日月昭发布了新的文献求助10
16秒前
蒋大饼发布了新的文献求助10
16秒前
心灵蛋花汤完成签到,获得积分10
17秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7582011
求助须知:如何正确求助?哪些是违规求助? 9161073
关于积分的说明 19601413
捐赠科研通 7164165
什么是DOI,文献DOI怎么找? 3266055
关于科研通互助平台的介绍 2430963
邀请新用户注册赠送积分活动 2257213