Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers.

A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser "DnOTDR" outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification.

Artificial neural networks for quantitative online NMR spectroscopy.

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. Graphical abstract.

Real-time dynamic strain sensing in optical fibers using artificial neural networks.

We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.

Wavelength-scanning coherent OTDR for dynamic high strain resolution sensing.

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.

Humidity-induced Brillouin frequency shift in perfluorinated polymer optical fibers.

We report, to our knowledge, for the first time on humidity-induced Brillouin frequency shifts in perfluorinated graded index polymer optical fibers. A linear relation between Brillouin frequency shift and humidity was observed. Furthermore, the humidity coefficient of the Brillouin frequency shift is demonstrated to be a function of temperature (-107 to -64 kHz/%r.h. or -426 to -49 kHz m3/g in the range of 20 to 60 °C). An analytical description proves temperature and humidity as two mutually independent effects on the Brillouin frequency shift.

Investigation on the Influence of Humidity on Stimulated Brillouin Backscattering in Perfluorinated Polymer Optical Fibers.

In this paper perfluorinated graded-index polymer optical fibers are characterized with respect to the influence of relative humidity changes on spectral transmission absorption and Rayleigh backscattering. The hygroscopic and thermal expansion coefficient of the fiber are determined to be C H E = (7.4 ± 0.1) · 10 - 6 %r.h.-1 and C T E = (22.7 ± 0.3) · 10 - 6 K-1, respectively. The influence of humidity on the Brillouin backscattering power and linewidth are presented for the first time to our knowledge. The Brillouin backscattering power at a pump wavelength of 1319 nm is affected by temperature and humidity. The Brillouin linewidth is observed to be a function of temperature but not of humidity. The strain coefficient of the BFS is determined to be C S = (-146.5 ± 0.9) MHz/% for a wavelength of 1319 nm within a strain range from 0.1% to 1.5%. The obtained results demonstrate that the humidity-induced Brillouin frequency shift is predominantly caused by the swelling of the fiber over-cladding that leads to fiber straining.

Relative change measurement of physical quantities using dual-wavelength coherent OTDR.

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.

Distributed Humidity Sensing in PMMA Optical Fibers at 500 nm and 650 nm Wavelengths.

Distributed measurement of humidity is a sought-after capability for various fields of application, especially in the civil engineering and structural health monitoring sectors. This article presents a method for distributed humidity sensing along polymethyl methacrylate (PMMA) polymer optical fibers (POFs) by analyzing wavelength-dependent Rayleigh backscattering and attenuation characteristics at 500 nm and 650 nm wavelengths. Spatially resolved humidity sensing is obtained from backscatter traces of a dual-wavelength optical time domain reflectometer (OTDR). Backscatter dependence, attenuation dependence as well as the fiber length change are characterized as functions of relative humidity. Cross-sensitivity effects are discussed and quantified. The evaluation of the humidity-dependent backscatter effects at the two wavelength measurements allows for distributed and unambiguous measurement of relative humidity. The technique can be readily employed with low-cost standard polymer optical fibers and commercial OTDR devices.

Fast fitting of reflectivity data of growing thin films using neural networks.

X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8-18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.