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- Distributed fiber optic sensing (2) (entfernen)
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The feasibility of traffic monitoring along a major urban road using Distributed Acoustic Sensing (DAS) is demonstrated. We present measurement results of conventional intensity-based DAS along a dark fiber in a tubed fiber optic cable buried roadside. With only minimal postprocessing different classes of traffic participants can be distinguished (cars, bikes, pedestrians). Different methods for pre-processing raw data are compared, especially with regard to providing suitable inputs for pattern recognition algorithms. Furthermore, the various challenges for automatized vehicle detection and classification related to varying sensitivity and inhomogeneous signal propagation are discussed. Sensitivity fluctuations and variations are in part inherent to the measurement technology and originate in part in ground conditions. The measurement quality and usefulness for traffic monitoring of intensity-based DAS is compared to that of state-of-the-art phase-resolved DAS, allowing quantitative evaluation of vibration signals.
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.