Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars. The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods. The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h. The analysis methods presented here will help to establish routines for near real-time train tracking and train integrity analysis.

Industrial piping systems are particularly relevant to public safety and the continuous availability of infrastructure. However, condition monitoring systems based on many discrete sensors are generally not well-suited for widespread piping systems due to considerable installation effort, while use of distributed fibre-optic sensors would reduce this effort to a minimum. Specifically distributed acoustic sensing (DAS) is employed for detection of third-party threats and leaks in oil and gas pipelines in recent years and can in principle also be applied to industrial plants. Further possible detection routes amenable by DAS that could identify damage prior to emission of medium are subject of a current project at BAM, which aims at qualifying distributed fibre optic methods such as DAS as a means for spatially continuous monitoring of industrial piping systems. Here, first tests on a short pipe are presented, where optical fibres were applied directly to the surface. An artificial signal was used to define suitable parameters of the measurement system and compare different ways of applying the sensor.

Pipe integrity is a central concern regarding technical safety, availability, and environmental compliance of industrial plants and pipelines. A condition monitoring system that detects and localizes threats in pipes prior to occurrence of actual structural failure, e.g., leakages, especially needs to target transient events such as impacts on the pipe wall or pressure waves travelling through the medium. In the present work, it is shown that fiber-optic distributed acoustic sensing (DAS) in conjunction with a suitable application geometry of the optical fiber sensor allows to track propagating acoustic waves in the pipeline wall on a fast time-scale. Therefore, short impacts on the pipe may be localized with high fidelity. Moreover, different acoustic modes are identified, and their respective group velocities are in good agreement with theoretical predications. In another set of experiments modeling realistic damage scenarios, we demonstrate that pressure waves following explosions of different gas mixtures in pipes can be observed. Velocities are verified by local piezoelectric pressure transducers. Due to the fully distributed nature of the fiber-optic sensing system, it is possible to record accelerated motions in detail. Therefore, in addition to detection and localization of threatening events for infrastructure monitoring, DAS may provide a powerful tool to study the development of gas explosions in pipes, e.g., investigation of deflagration-to-detonation-transitions (DDT).

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