TY - CONF A1 - Lu, Xin A1 - Schukar, Marcus A1 - Krebber, Katerina T1 - Characterizing vibration response of fiber cables for distributed acoustic sensing T2 - Proceedings 27th International Conference on Optical Fiber Sensors N2 - The vibration responses of two fiber cables are characterized up to 16 kHz and compared with a standard tight-buffered 900 um fiber. The response of the cables is suppressed due to the cable protection T2 - 27th International Conference on Optical Fiber Sensors (OFS-27) CY - Alexandria, VA, USA DA - 29.08.2022 KW - Response characterization KW - Distributed fiber sensing KW - Acoustic sensing PY - 2022 SP - 1 EP - 4 PB - Optica CY - Washington D.C., USA AN - OPUS4-56096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Königsbauer, Korbinian A1 - Nöther, N. A1 - Schaller, M. B. A1 - Wosniok, Aleksander A1 - Krebber, Katerina T1 - Distributed POF sensors for structural health monitoring in civil construction applications T2 - POF conference proceedings N2 - In this paper, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is presented. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is developed for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and cost-effective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber. T2 - POF 2022 The 30th International Conference on Plastic Optical Fibers CY - Bilbao, Spain DA - 26.09.2022 KW - I-OFDR KW - PF-POF KW - Structural health monitoring KW - Rayleigh backscatter measurement KW - Strain detection PY - 2022 VL - 2022 SP - 152 EP - 155 AN - OPUS4-56060 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lu, Xin A1 - Schukar, Marcus A1 - Weege, S. A1 - Roske, T. A1 - Krebber, Katerina T1 - Leakage detection at a borehole simulator using distributed acoustic sensing T2 - Proceedings 27th International Conference on Optical Fiber Sensors N2 - A distributed acoustic sensing system is used to detect the leakage in a borehole simulator by measuring the leakage induced vibration. The leakage location can be clearly determined by frequency spectrum analysis. T2 - 27th International Conference on Optical Fiber Sensors (OFS-27) CY - Alexandria, VA, USA DA - 29.08.2022 KW - Leakage detection KW - Structural health monitoring KW - Distributed fiber sensing KW - Distributed acoustic sensing PY - 2022 SP - 1 EP - 4 PB - Optica CY - Washington D.C., USA AN - OPUS4-56097 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Temperature and humidity discrimination in Brillouin distributed fiber optic sensing using machine learning algorithms T2 - Optical Sensing and Detection VII N2 - In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection. T2 - SPIE Photonics Europe 2022 CY - Strasbourg, France DA - 03.04.2022 KW - Distributed Brillouin sensing KW - Machine learning KW - Artificial neural networks KW - Simultaneous temperature and humidity sensing KW - BOFDA PY - 2022 DO - https://doi.org/10.1117/12.2620985 VL - 12139 SP - 1 EP - 7 PB - SPIE AN - OPUS4-54861 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -