TY - JOUR A1 - Fricke, F. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Hierzegger, R. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy KW - Modular Production PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 SN - 2168-6750 VL - 10 IS - 1 SP - 87 EP - 98 PB - IEEE AN - OPUS4-53941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kowarik, Stefan A1 - Hussels, Maria-Teresa A1 - Chruscicki, Sebastian A1 - Münzenberger, Sven A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. T1 - Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis N2 - 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. KW - Artificial neural networks KW - Distributed fiber optic sensing KW - Distributed acoustic sensing KW - Train tracking PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-502872 VL - 20 IS - 2 SP - 450 PB - MDPI CY - Basel, Switzerland AN - OPUS4-50287 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - 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. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-507508 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, C. A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Pithan, L. A1 - Liehr, Sascha A1 - Schreiber, Frank A1 - Kowarik, Stefan T1 - Fast fitting of reflectivity data of growing thin films using neural networks N2 - 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 alpha-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. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-498997 SN - 1600-5767 VL - 52 SP - 1342 EP - 1347 PB - Wiley AN - OPUS4-49899 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kockert, M. A1 - Mitdank, R. A1 - Zykov, A. A1 - Kowarik, Stefan A1 - Fischer, F. T1 - Absolute Seebeck coefficient of thin platinum films N2 - The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt. KW - Thin magnetic films PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-499007 SN - 0021-8979 VL - 126 SP - 105106 PB - AIP AN - OPUS4-49900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Jäger, L. A. A1 - Karapanagiotis, Christos A1 - Münzenberger, Sven A1 - Kowarik, Stefan T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks N2 - 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. KW - Distributed vibration sensing KW - Distributed acoustic sensing KW - Coherent optical time domain reflectometry KW - Optical fiber sensor KW - Artificial neural network KW - Real time measurement KW - Distributed strain sensing PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-474832 UR - https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 SN - 1094-4087 VL - 27 IS - 5 SP - 7405 EP - 7425 PB - Optical Society of America AN - OPUS4-47483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -