外文翻译--结合近年来对基金申请纤维光学传感器的安全监测

附录 Recent applications of fiber optic sensors to health monitoring in civil engineering Hong-Nan Li a, Dong-Sheng Li a, Gang-Bing Song a,b A State Key Laboratory of Coastal and Offshore Engineering, Department of Civil and Hydraulic Engineering, Dalian University of Technology,Gan jing zi district, Ling gong Road 2, Dalian 116024, China Department of Mechanical Engineering, University of Houston, Houston, TX 77204-4006, USA Received 10 January 2003; received in revised form 11 May 2004; accepted 25 May 2004Abstract Abstract This paper presents an overview of current research and development in the field of structural health monitoring with civil engineering applications. Specifically, this paper reviews fiber optical sensor health monitoring in various key civil structures,including buildings, piles, bridges, pipelines, tunnels, and dams. Three commonly used fiber optic sensors (FOSs) are briefly described. Finally, existing problems and promising research efforts in packaging and implementing FOSs in civil structural health monitoring are discussed. 2004 elsevier Ltd. All rights reserved. Keywords: Structural health monitoring Fiber optic sensor Civil health 1. Introduction Structural health monitoring has attracted much attention in both research and development in recent years. This reflects continuous deterioration conditions of important civil infrastructures, especially long-span bridges. Among them, many were built in the 1950s with a 40- to- 50-year designed life span. The collapses and failures of these deficient structures cause increasing concern about structural integrity, durability and reliability, i.e. the health of a structure throughout the World. Currently, there are no foot proof measures for structural safety. A structure is tested for deteriorations and damages only after signs that result from fault accumulations are severe and obvious enough. When the necessity of such tests becomes obvious, damages have already exacerbated the systems reliability in many cases and some structures are even on the verge of collapse. Though routine visual inspection is mandatory for important structures in some countries, for instance, bridges in the US, its effectiveness in finding all the possible defects is questionable. A recent survey by Moore et al. 1 of the US Federal Highway Administration revealed that at most 68% of the condition ratings were correct and in-depth inspections could not find interior deficiencies considering the fact that visual examination by inspectors barely exists. Structural health monitoring (SHM) refers to the use of in-situ, continuous or regular (routine) measurement and analyses of key structural and environmental parameters under operating conditions, for the purpose of warning impending abnormal states or accidents at an early stage to avoid casualties as well as giving maintenance and rehabilitation advice. This tentatively proposed definition of SHM complements that given by housner 2. This definition emphasizes the essence of the advance alert ability of SHM. In general, a typical SHM system includes three major components: a sensor system, a data processing system (including data acquisition, transmission and storage), and a health evaluation system (including diagnostic algorithms and information management). The sensors utilized in SHM are required to monitor not only the structural status,for instance stress,displacement,acceleration etc,but also influential environmental parameters, such as wind speed, temperature and the quality of its foundation. Since a large number of sensors will be involved in a health monitoring system, the acquisition, transmission and storage of a large quantity of data for such continuous monitoring is a challenging task. For instance, raw data are acquired at a rate of63.46 MB per hour for the TsingMa and Kap Shui Mun Bridges and 55.87 MB per hour for TingKau Bridge 3. Therefore, many wireless 4,5, GPS 6 or GIS 7 based data acquisition,transmission methods and data archival and management architectures 8 were proposed to deal with this problem. Though it is very important to embed sensors and collect data successfully for a health monitoring application, the final step is to interpret correctly the data from various types of sensors to reach critical decisions regarding the load capacity, system reliability, i.e. the health status of the structure 9.At this crucial step, prognostic and diagnostic algorithms based on modal analysis, pattern recognition and time series analysis are among the most effective methods to detect the presence, location, magnitude, and extent of structural faults 10. Moreover, the information analyzed should be user friendly to improve operation and maintenance management decisions.Another crucial function of SHM is the ability to alert ongoing dangers or future accidents in advance. Though it is not a simple task to realize fully such an appealing scenario, several projects had been undertaken to implement partially SHM systems from research laboratories to field applications. TsingMa, Kap Shui Mun, and TingKau bridges, connecting Hong Kong and its new airport, are the most noteworthy bridges being heavily instrumented for health monitoring. Wind load is a major concern of these bridges as well as temperature, traffic load, geometric configuration, strain, and global dynamic characteristics.Among the 786 permanently installed sensors in TsingMa Bridge, anemometers, temperature, strain and accelerometer sensors comprise a major portion. Monitoring results are satisfactory and have verified design performance 11. Similar sensors were also used in the health monitoring system of the Akashi Kaikyo Bridge in Japan. The transversal displacement of5.17 m monitored in September 1998 agreed well with numerical simulations. Commodore Barry Bridge and Benicia- Martinez bridge of the US are equally important examples of SHM 12. In Commodore Barry Bridge, real-time images and data from nearly 500 channels combined with its finite element model are used for maintenance and management of the bridge to the maximum benefit. Other significant efforts in implementing health monitoring systems include bridges in Korea, Canada, India and Colombia 13. Most of the conventional sensors used in the above mentioned health monitoring applications are based on transmission of electric signals. Their limitations are becoming more and more manifest. These sensors are usually not small or durable enough to be embedded in a structure to measure interior properties. They are local (or point) sensors, which are restricted to measure only parameters at one location and cannot be easily multiplexed. The long lead lines also pose problems for large civil structures, which often span several or tens of kilometers. In some cases, the signals could not be discriminated from noise because of electrical or magnetic interference (EMI). In addition, various demodulation techniques are required for different sensors. They all add in increasing the inconveniences of conventional sensors in SHM. Fiber optic sensors (FOSs) are promising sensing alternatives in civil SHM systems and future smart structures. They exhibit several advantages such as, flexibility, embeddability, multiplexity and EMI immunity 14, as compared with traditional sensors. The past 20 years have witnessed an intense international research in the field of optical fiber sensing. In the following sections, we will describe this enabling technology and review its health monitoring applications to civil engineering. 2. Three fiber optic sensors for structural health Monitoring The first fiber optic sensor, a closed-loop fiber gyro,was invented to replace mechanical spinning gyros on the Delta Rocket in 1978 15. Conception of such FOSs originated from fiber optic communications. Optical fiber experiences geometrical (size and shape) and optical (refractive index and mode conversion)changes due to various environmental perturbations while conveying light from one place to another. These phenomena perplexed efforts to minimize such adverse influences so that signal transmission is smooth and reliable. However, it is found that such optical changes can be employed to measure the external environment parameters. Optic fiber thus found its niche in sensor applications. Investigations showed that the sensitive perturbations in temperature, strain, rotation, electric and magnetic currents, etc., can be converted or encoded into corresponding changes, such as amplitude(intensity), phase, frequency, wavelength and polarization in the optical properties of the transmitted light.These changes can be eventually detected by appropriate demodulation systems 16,17. With rapid advances in communication and start of mass production of fiber optic cables, fiber optic sensing is growing to be a prosperous industry, benefiting from the decreasing fiber prices. Many techniques have been devised to provide solutions to measure a broad range of physica l and chemical parameters. As a consequence, fiber optic based measurement systems have made the transition from research laboratories to practical engineering applications, and have found wide applications in aerospace,composites, medicine, chemical products, concrete structures, and in the electrical power industry. The market volume of FOSs is hypothesized to rise from US$ 305 millions in 1997 to this years US$ 550millions 18, among which temperature, strain and pressure sensors account for about 40% of the total FOS products 19. Extensive efforts are now engaged to realize economic FOSs and associated interrogation systems and to explore wider engineering applications. Optical fibers, which usually consist of three layers: fiber core, cladding and jacket, are dielectric devices used to confine and guide light. The majority of optical fibers used in sensing applications have silica glass cores and claddings, and the refractive index of the cladding is lower than that of the core to satisfy the condition of Snells law for total internal reflection and thus confine the propagation ofthe light along the fiber core only. The outer layer of a FOS, called jacket, is usually made of plastic to provide the fiber with appropriate mechanical strength and protect it from damage or moisture absorption. In some sensing applications, a specialized jacket is required to enhance the fibers measurement sensitivity and to accommodate the host structure. In general, an FOS is characterized by its high sensitivity when compared to other types of sensors. It is also passive in nature due to its dielectric construction. Specially prepared fibers can withstand high temperature and other harsh environments. In telemetry and remote sensing applications, it is possible to use a segment of the fiber as a sensor gauge and a long length of the same or another fiber to convey the sensed information to a remote station. Deployment of distributed and array sensors covering extensive structures and geographical locations is also feasible. With many signal processing devices (splitter, combiner, multiplexer, filter, delay line, etc.) being made of fiber elements, an all-fiber measuring system can be realized. Table 1 lists the FOSs available to civil engineering applications and their categories. One method of classifying FOSs is based on its light characteristics(intensity, wavelength, phase, or polarization) that is affected by the parameter to be sensed. Another method classifies an FOS by whether the light in the sensing segment is modified inside or outside the fiber(intrinsic or extrinsic). FOSs can also be classified as local (or point), quasi-distributed and distributed sensors depending on the sensing range. This method of classification is adopted here to organize the rest of this section. 2.1. Local fiber optic sensors Many intensity based sensors, such as microbend sensors,and most of the interferometric FOSs are local sensors, which can measure changes at specified local points in a structure. Interferometric FOSs are by far the most commonly used local sensors since they offer the best sensitivity. This sensing technique is based primarily on detecting the optical phase change induced in the light as it propagates along the optical fiber. Light from a source is equally divided into two fiber-guided paths (one is a reference path). The beams are then recombined to mix coherently and form a fringe pattern which is directly related to the optical phase difference experienced between the two optical beams. The most common configurations of such interferometric sensors are the Mach-Zehnder, Michelson and FabryPerot FOSs 20,21. Among them, the FabryPerot (F-P cavity) FOS and the so-called long gage FOS (LGFOSs) are the two types oflocal sensors commonly utilized in civil engineering. FabryPerotFOSs, which can provide absolute FabryPerot cavity length measurements with superior accuracy (Fig. 1),are based on white-light cross-correlation principle. In addition to its strain-sensing ability, an F-P sensor can also measure pressure, displacement and temperature with different configurations. LGFOSs are based on two low-coherent double Michelson interferometers (Fig. 2). Both sensors measure the average strain between two fixed points along the gage with optional temperature compensation. The length of the long gage sensors ranges from 0.2 to 50 m. 结合近年来对基金申请 纤维 光学传感器的安全监测 李宏男 李东升 宋钢兵 国家重点实验室的沿海和海洋工程 土木工程系和水利工程系 。大连理工大学 中国大连甘井子区 凌工路 2 号 116024 机械工程系 美国休斯敦大学 ,休斯顿 ,德克萨斯州 77204 - 4006 收到时间： 2003 年的一月十日 修订后的形式收到时间： 2004 年 5 月 11 最终接受时间： 2004 年 5 月 25 日 摘 要 文章概述了目前研究开发的结构安全监测领域与土木工程领域。具体地说 ,本文回顾了光纤传感器的安全监测各种民用建筑。包括建筑物 ,桩、桥梁、管道、隧道、水坝。简要的描述常用的三种光纤传感器 (自由 /开源软件 )。最后 ,存在的问题和广阔的研究成果在包装和实施自由 /开源软件在土木结构安全监测进行了讨论 2004 年教育部博士点基金有限公司版权所有 关键词 : 结构安全监测 光纤传感器 民用的安全 1.介绍 结构安全最近几年的研究和发展吸引了很多监测机构的关注。这反映了不断恶化的条件这 些土建结构不变的 ,尤其是大跨径桥梁。其中 ,很多结构都建于 1950年代和一般是 40 到 50 的设计寿命。伴随着这些缺陷和不足 ,在以后的安全结构则会更注重完整性 ,耐用性和可靠性。 目前 ,没有办法即刻证明结构的安全性。结构进行恶化和损害迹象之后，这些结果表明从严重故障中积累经验效果还是不够明显的。当测试的必要性变得越来越明显 ,由于损害赔偿责任已经提高了系统的可靠性。许多情况下 ,一些建筑甚至处于崩溃的边缘。在一些国家重要结构是需要强制性的常规视觉检测 ,比如桥梁在美国 ,其发现所有可能的缺陷的有效性是值得商榷的。 最近一项摩尔的调查表明 。 1美国联邦公路管理局显示的最高只有 68%的状态评级是正确的，不深入检查不能 找到内部缺陷，即使已经考虑到视觉检查检验存在。几乎所有的结构安全监测 (单孔位微吹气扰动 )都是指的是使用的现场、连续或定期 (常规 )测量并分析了关键结构和环境参数在常规的操作条件下 ,为目的警告即将在异常或事故早期和晚期避免人员伤亡以及时给予维护和康复的建议。初步提出了并定义了单孔位微吹气扰动定义 2。 这个定义强调的是单孔位微吹气扰动的警觉本质进步能力。 一般来说 ,一个典型的单孔位微吹气扰动系统包括三 个主要成份 :一个传感器系统 ,数据处理系统 (包括数据采集、传输和存储 ),和安全评估体系 (包括诊断算法和信息管理 )。传感器是利用只需要监视单孔位微吹气扰动时结构状况 ,例如压力 ,位移、加速度等 ,但同时也有影响力环境参数 ,例如风速、温度和高质量的基础。由于大部分感觉遥感数字将会积极参与的在一个安全监测系统 ,采集、传输和储存大量数据这样很有挑战性的不间断的监控任务。例如 ,原始数据获得每小时 63.46 MB 速度的 青马桥 和汲水门桥梁和以每小时 55.87 MB 的 汀九 桥 3。因此 ,许多无线 4、 5)、 GPS67或地 理信息系统 (GIS)的基础数据采集 ,传播手段和数据档案 8管理架构提出了方法解决这个问题。尽管它很成功嵌入传感器并在数据采集和安全监测中广泛应用 ,最后一步是翻出来正确数据从各种类型的传感器达到关键性的部位对于负载能力、系统可靠性 ,即要求有较高安全状况的结构 9。在这个重要的一步 ,预后与诊断算法基于模态分析、模式识别方法和时间序列分析是最为有效的方法来探测是否存在错位、规模和断层程度 .10。此外 ,另一个重要的功能是应用传来的信息分析提高操作和维护管理。单孔位微吹气将在扰动警惕危险事故中持续或未来的结构 中广泛应用。它并不是一个简单的任务 ,实现完全这样的吸引人的情况下 ,几个项目已经在部分研究实验室进行单孔位微吹气扰动系统实施在现场中的应用的实验。青马 ,汲水门、和汀九桥梁 ,连接香港和新的机场 ,是被装有大量有益于安全监测的最值得关注的桥梁。在桥的温度、流量负荷、几何配置、应变、全球动态特性等中风荷载是一个重要的问题 . 在安装 786 个的永久传感器的青马桥 ,是由 风速计 、温度、应变加速度计传感器主要部分组成。监测结果是令人满意的 ,验证了设计的性能 11。类似的传感器也被使用在正式通车日本石海峡大桥的安全监测。 1998 年 9 月巴里桥同意监测和数值进行模拟所得横向位移为 5.17 米。例如 -准将马丁内兹桥都是我们是单孔位微吹气扰动的同等重要的实例 .12。巴里司令大桥 ,实时图象、数据来自近500 个频道结合的有限元模型用于维护和管理桥梁。 在执行其他重大的桥梁安全监测系统国家包括韩国、加拿大、印度和哥伦比亚 .13.传统的传感器最主要的用于上述提到安全监测应用的基础所传播的信号。其局限性是变得越来越明显。 这些传感器通常粘贴在耐用的一个较小结构的内部进行测量 .这些 (或点 )传感器的限制量只局限在一个位置参数而不容易多路复用。 大型民用建筑长跨度问题也随之而来 ,经常跨几个至数十个公里。在某些情况下 ,信号会遭受噪音的磁场干扰 (EMI)。此外 ,不同解调技术对传感器要求不同。在传统传感器都加入了单孔位微吹气扰动。光纤传感器 (自由 /开源软件 )是有希望的应用在单孔位微吹气扰动系统的传感和未来的智