Distributed vibration sensing in optical fibers opened entirely new opportunities and penetrated various sectors from security to seismic monitoring. Here, we demonstrate a most simple and robust approach for dynamic strain measurement using wavelength-scanning coherent optical time domain reflectometry (C-OTDR). Our method is based on laser current modulation and Rayleigh backscatter shift correlation. As opposed to common single-wavelength phase demodulation techniques, also the algebraic sign of the strain change is retrieved. This is crucial for the intended applications in structural health monitoring and modal analysis. A linear strain response down to 47.5 pε and strain noise of 100 pε/√Hz is demonstrated for repetition rates in the kHz range. A field application of a vibrating bridge is presented. Our approach provides a cost-effective high-resolution method for structural vibration analysis and geophysical applications.
Artificial neural networks trained with simulated data are shown to correctly and quickly determine film parameters from experimental X-ray reflectivity curves.
We present, to our knowledge for the first time, the possibility of measuring the backscatter signal of perfluorinated polymer optical fibers (POF) using an incoherent optical frequency domain reflectometry (OFDR) technique. The OFDR setup is described and it is shown that the dynamic range and measurement speed are superior to standard OTDR systems. It is shown for the first time that distributed detection of strain in POF is possible using the OFDR technique.
We propose the use of alternating pulse wavelengths in a direct-detection coherent optical time domain reflectometry (C-OTDR) setup not only to measure strain and temperature changes but also to determine the correct algebraic sign of the change. The sign information is essential for the intended use in distributed mode shape analysis of civil engineering structures. Correlating relative backscatter signal shifts in the temporal/signal domain allows for measuring with correct magnitude and sign. This novel approach is simulated, experimentally implemented and demonstrated for temperature change measurement at a spatial resolution of 1 m.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. Keywords Online NMR spectroscopy . Real-time process monitoring . Artificial neural networks . Automation . Process industry Abbreviations A hf Measured high-field NMR areas (reference) A hf,test Measured high-field NMR areas (reference) of test dataset A hf,val Measured high-field NMR areas (reference) of validation dataset A lf,ANN Predicted areas of ANN model A lf,ANN,val Predicted areas of ANN model for validation dataset (S lf,val ) A lf,IHM Predicted areas of IHM A lf,synth Areas of pure components of synthetic mixture spectra k Index for component model K Total number of pure component models NN i/ii
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