Trackside Optical Fibre Acoustic Sensing (TOFAS)

分布式光纤声学和动态应变传感在安全、能源、环境和交通运输等行业有着重要的应用。这类应用的例子包括入侵者检测、石油/天然气管道和核动力反应堆系统中的泄漏检测、监测水力压裂引起的冲击波，以及跟踪和监听行驶中的列车。所有分布式光纤传感的主要优势是可以在光纤沿线的每个点检测到被测对象。通过这种方式，可以用一根光纤简单地取代大量的分立传感器。基本操作基于沿光纤传感光纤发送光脉冲，并检测由待测量参数引起的后向散射光的变化。瑞利背向散射光对光纤上的声压应变很敏感。由于瑞利背向散射道上的每个点对应于传感光纤的一段，因此可以通过以规则的间隔向传感光纤发射光脉冲并监测背向散射道的变化来映射沿着传感光纤的声波波场。利用合适的光学装置和我们开发的数字信号处理技术，声感应应变的频率、相位和幅度可以沿传感光纤的整个长度进行空间分辨。脉冲的重复率确定重复测量的频率，从而确定声信号的检测带宽。本质上，单根光纤可以执行与多个(~10000)麦克风相同的功能，但大大降低了成本和安装的复杂性。拟议的研究是开发一种分布式光纤声学和动态应变传感器技术，其能力远远超过目前已实现的能力，以提高其对一些关键应用的适用性，特别是对轨道交通行业监测轨道和列车健康的适用性。改进的原因将是修改光学配置和采用新的数据处理和处理硬件和软件。虽然未来铁路运输的预期增长将不可避免地需要铁路基础设施的额外增长，但业界必须继续努力提高现有列车服务的效率，同时保持最高的安全标准。这项建议旨在发展最先进的分布式光纤声学传感，目标是：i)准确确定列车的位置和速度，从而优化列车密度；ii)检测异常声音，及早指示潜在问题，如入侵者、电缆被盗、松动和嘎嘎作响的部件等，以便及时进行维护或采取预防措施，将干扰降至最低；ii)监测轨旁机器的状况，如道口电机和远程发电机，以确保安全和高效的运营。实现这些目标将有助于提供安全、高效和可靠的铁路运输，使现有基础设施的容量最大化。

Distributed fibre optic acoustic and dynamic strain sensing has important applications in the security, energy, environment and transport industries. Examples of such applications include intruder detection, leak detection in oil/gas pipelines and nuclear power reactor systems, monitoring shock waves caused by fracking, and tracking and listening to moving trains. The key advantage of all distributed fibre optic sensing is that a measurand can be detected at every point along the fibre. In this way a large number of discrete sensors can be simply replaced by a single optical fibre. The basic operation is based on sending pulses of light down the optical sensing fibre and detecting the changes in the backscattered light, caused by the parameters to be measured. The Rayleigh backscattered light is sensitive to the sound pressure induced strain on the fibre. Since each point on the Rayleigh backscattered trace corresponds to one section of the sensing fibre, the acoustic wave field along the sensing fibre can be mapped by launching optical pulses into the sensing fibre at a regular intervals and monitoring the changes in the backscattered traces. With the appropriate optical setup and the digital signal processing that we have developed, the acoustically induced strain in terms of its frequency, phase and amplitude can be spatially resolved along the entire length of the sensing fibre. The repetition rate of the pulses determines the frequency at which the measurement is repeated and hence the detection bandwidth of the acoustic signal. In essence, the single optical fibre can perform the same function as multiple (~10000) microphones but with much reduced cost and complexity of installation. The proposed research is to develop a distributed fibre optic acoustic and dynamic strain sensor technology with capabilities far in excess of what has currently been achieved in order to improve its applicability to a number of key applications, but in particular to the rail transport industry for monitoring the health of track and trains. The improvement will stem from modification of the optical configuration and introduction of new hardware and software for data handling and processing. Whilst future predicted growth in rail travel will inevitably require additional growth in rail infrastructure, it is imperative that the industry continues to strive to improve the efficiency of existing train services, whilst maintaining the highest of safety standards. This proposal is concerned with developing the state of the art distributed fibre optic acoustic sensing and with the goal of enablingi) Accurate determination of the location and speed of trains which will allow train density to be optimised; ii) Abnormal sounds to be detected, providing early indication of potential problems such as intruders, cable theft, loose and rattling components, etc, facilitating timely maintenance or preventative action to minimize disruptions; andiii) The condition of track-side machines such as level crossing motors and remote generators to be monitored, ensuring safe and efficient operation.Achieving these goals will help to provide safe, efficient and reliable rail transport that maximises the capacity of the existing infrastructure.