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Es handelt sich hierbei um die erste Veröffentlichung einer verifizierbaren eAttestation (DCC mit DAkkS-Siegel). Sie wurde vom akkreditierten Kalibrierlabor D-K-11075-08, im Rahmen des QI-Digital Vorhabens, für den Fachbereich 8.1 erstellt und dient im Zusammenhang mit dem Vortrag "Digitale Kalibrierzertifikate – die Umsetzung in akkreditierten Laboratorien" auf dem 324. PTB-Seminar "Berechnung der Messunsicherheit – Empfehlungen für die Praxis" als Anschauungsmaterial für die Digitalisierung in der Metrologie.
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.