TY - CONF A1 - Liehr, Sascha A1 - Burgmeier, J. ED - Jaroszewicz, L.R. T1 - Quasi-distributed measurement on femtosecond laser-induced scattering voids using incoherent OFDR and OTDR N2 - We propose to use focused femtosecond laser pulses to create scattering damage in standard singlemode optical fibres as reference points for quasi-distributed sensing applications. Such sensor fibres are interrogated with incoherent optical frequency domain reflectometry (I-OFDR) technique and optical time domain reflectometry (OTDR). A performance comparison of both techniques with the clear advantage of the I-OFDR is presented as well as a quasi-distributed length change measurement application. Also dynamic measurement based on the I-OFDR technique is demonstrated on a femtosecond laser-induced sensor chain. T2 - EWOFS 2013 - 5th European workshop on optical fibre sensors CY - Kraków, Poland DA - 19.05.2013 KW - Optical fiber sensors KW - Femtosecond laser KW - Strain sensor KW - OFDR KW - OTDR PY - 2013 SN - 978-0-81949-634-8 DO - https://doi.org/10.1117/12.2026780 SN - 0277-786X N1 - Serientitel: Proceedings of SPIE – Series title: Proceedings of SPIE VL - 8794 IS - Paper 8794 - 181 SP - 87943N-1 EP - 87943N-4 AN - OPUS4-28734 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha T1 - Quasi-distributed measurement on femtosecond laser-induced scattering voids using incoherent OFDR and OTDR T2 - 5th European Workshop on Optical Fiber Sensors CY - Kraków, Poland DA - 2013-05-19 PY - 2013 AN - OPUS4-28742 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha T1 - Quasi-distributed fiber bend and temperature measurement in femtosecond laser-structured POF T2 - 22nd International Conference on Plastic Optical Fibers CY - Buzios, Brasil DA - 2013-09-11 PY - 2013 AN - OPUS4-29504 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Gründer, Klaus-Peter A1 - Hille, Falk A1 - Liehr, Sascha A1 - Maack, Stefan A1 - Niederleithinger, Ernst A1 - Pirskawetz, Stephan A1 - Rogge, Andreas A1 - Said, Samir A1 - Viefhues, Eva A1 - Zorn, Sebastian ED - Baeßler, Matthias ED - Möller, G. ED - Rogge, Andreas ED - Schiefelbein, N. T1 - Innovative Messsysteme zum Brückenmonitoring am Beispiel einer Versuchsbrücke – vom Sensor bis zur Zustandsprognose N2 - In der aktuellen Projektphase liegt der Fokus auf der Datenaufnahme, -bearbeitung und -speiche-rung mit dem Ziel, automatisierte Auswerteverfahren einsetzen zu können. Aktuell wurden primär punktuelle Messungen an ausgewählten Messtagen aufgenommen. Die Systeme sollen so weiter-entwickelt werden, dass sie sich auch für kontinuierliche Messungen im Rahmen von Monitoring-aufgaben eignen. Ein wichtiger Fokus bei der Auswertung ist die Kombination mit numerischen Modellen, die mithilfe von Bayesian Update Verfahren und den aufgenommenen Messdaten kalibriert und im Verlauf der Monitoringaufgabe angepasst werden sollen. Insbesondere sollen auch zeitabhängige Modelle, die eine zeitliche Entwicklung von Struktureigenschaften beinhalten (Kriechen, Schwinden, Ermüdung, Korrosion) dazu verwendet werden, die zukünftige Performance der Struktur bewerten zu können. Basierend darauf werden dann Konzepte zur Planung von Inspektion und Wartung erstellt. T2 - Messen im Bauwesen 2017 CY - Berlin, Germany DA - 14.11.2017 KW - Brückeninfrastruktur KW - Innovative Messverfahren KW - Stereofotogrammetrie PY - 2017 SN - 978-3-9818270-8-8 VL - 2017/1 SP - 7 EP - 19 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-44306 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 DO - https://doi.org/10.1109/TETC.2021.3131371 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 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, S. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are 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 developed 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-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible 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. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Grenoble, France DA - 01.02.2021 KW - Industry 4.0, KW - Cyber-physical systems KW - Artificial neural networks KW - Mass spectrometry KW - Nuclear magnetic resonance spectroscopy PY - 2021 DO - https://doi.org/10.23919/DATE51398.2021.9473958 SP - 615 EP - 620 PB - IEEE AN - OPUS4-55360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - THES A1 - Liehr, Sascha T1 - Fibre Optic Sensing Techniques Based on Incoherent Optical Frequency Domain Reflectometry N2 - Diese Arbeit beschreibt einen alternativen Ansatz zur weit verbreiteten optischen Zeitbereichsreflektometrie, engl.: optical time domain reflectometry (OTDR). Die inkohärente optische Frequenzbereichsreflektometrie, engl.: incoherent optical frequency domain reflectometry (I-OFDR), wird grundlegend analysiert und hinsichtlich ihrer Möglichkeiten zur kontinuierlich ortsaufgelösten (verteilten) Rückstreumessung in optischen Fasern sowie faseroptischen Sensoranwendungen betrachtet. Im Gegensatz zum OTDR Ansatz wird hier die Übertragungsfunktion der optischen Faser gemessen, die über die inverse Fouriertransformation mit der äquivalenten Zeitbereichsantwort verknüpft ist. Das grundsätzliche Verfahren hat gewisse Vorteile und wird bereits zur Messung nichtlinearer optischer Effekte in optischen Fasern genutzt. Die allgemeine Rückstreumesstechnik unterscheidet sich jedoch bezüglich Anforderungen und Einschränkungen und wurde bisher nicht genau untersucht. Verteilte faseroptische Sensoranwendungen mit bemerkenswerter Messauflösung basierend auf Rayleigh-Rückstreuung und Reflexionsstellen in optischen Fasern werden erstmals vorgestellt. Im ersten Teil der Arbeit wird der Frequenzbereichsansatz theoretisch beschrieben. Notwendige Signalverarbeitung und deren Einfluss auf die Zeitbereichsantwort werden dargestellt. Abweichungen von der Linearität des I-OFDR Systems werden diskutiert und ein optimierter Messaufbau wird eingeführt; der entscheidende Einfluss der spektralen Eigenschaften der optischen Quelle wird im Detail betrachtet. Geeignete Parameter des I-OFDR Ansatzes, wie Dynamikbereich und Empfindlichkeit, werden definiert und für den Laboraufbau bestimmt. Ein möglicher Ansatz zur Unterdrückung starker Störsignale wird vorgestellt. Die Vorteile des I-OFDR Ansatzes gegenüber der OTDR Technik bezüglich der Umsetzung für hohe Ortsauflösungen sowie Messauflösung und Signalstabilität werden gezeigt. Diese Vorteile und dem Frequenzansatz eigene Messmöglichkeiten werden im zweiten Teil der Arbeit für ortsaufgelöste Sensoranwendungen demonstriert: Eine dämpfungsarme polymeroptische Faser (POF) wird erstmals auf ihre Sensoreigenschaften untersucht und zur verteilten Dehnungsmessung verwendet. Die Abhängigkeit der Rückstreuleistung von der aufgebrachten Dehnung kann genutzt werden, um gedehnte Faserstrecken zu lokalisieren. Weiterhin wird ein Korrelationsalgorithmus eingeführt, der es ermöglicht ortsaufgelöst Längenänderungen entlang der Faser mit mm-Auflösung zu messen indem die starken Streuzentren in der Faser mit einer Referenzmessung korreliert werden. Untersuchungen auf Querempfindlichkeiten der Sensorfaser bezüglich Temperatur, relativer Feuchte und Modenausbreitung zeigen vernachlässigbare bzw. beherrschbare Abhängigkeiten. In Kombination mit dem hochauflösenden I-OFDR Ansatz ermöglichen die vorgestellten Sensorverfahren vielversprechende neue Messanwendungen. Spezielles Interesse besteht in Bereichen der Bauwerksüberwachung, da die Faser nahezu verlustfrei auf über 100 % gedehnt werden kann. Ein weiteres Sensorverfahren, basierend auf dem Frequenzbereichsansatz zur dynamischen und quasi-verteilten Messung von Längenänderungen und Leistungsänderungen zwischen Reflexpunkten in der Faser wird präsentiert. Basierend auf der Messung weniger Frequenzpunkte der komplexen Frequenzantwort der Messfaser können mehrere Reflexe gleichzeitig und unabhängig voneinander bezüglich Position und reflektierter optischer Leistung ausgewertet werden. Messfrequenzen bis zu 2 kHz können erreicht werden und Längenänderungsauflösungen im μm-Bereich bei kleineren Messfrequenzen sind möglich. Das Messverfahren wird auf systematische Fehlereinflüsse untersucht und anhand von Demonstratormessungen validiert. Messungen der Deformation eines Gebäudes auf einem Erdbebenversuchsstand demonstrieren die Möglichkeit der Feldanwendung des Verfahrens. Das vorgestellte I-OFDR Verfahren demonstriert konkurrenzfähige Messparameter für allgemeine und hochauflösende optische Rückstreumessungen und die vorgestellten faseroptischen Sensorprinzipien zeigen vielversprechende Perspektiven für Anwendungen z.B. in der Bauwerksüberwachung. N2 - In this thesis, an alternative approach to the well-known optical time domain reflectometry (OTDR) technique is presented. A thorough analysis regarding distributed backscatter measurement in optical fibres is provided and its prospects for optical fibre sensing applications are demonstrated and discussed. The measurement approach is referred to as incoherent optical frequency domain reflectometry (I-OFDR): the frequency response of the fibre under test is measured and transferred into its time domain equivalent using inverse Fourier transform. This general technique has been studied and used for the measurement of nonlinear scattering effects in optical fibres. The requirements, limitations and prospects for general backscatter measurement, however, are different and have not been studied in detail prior to this work. Distributed sensing using Rayleigh scattering and reflective events in the fibre is first demonstrated using I-OFDR with remarkable measurement resolution. The incoherent detection technique allows for measuring singlemode fibres as well as multimode fibres. The first part of this work deals with the theoretical analysis and optimized implementation of the frequency domain approach. Necessary signal processing and its impact on the time domain response are presented. Sources of deviation from the linearity of the I-OFDR system are identified and an optimized laboratory setup is introduced; the crucial impact of the source coherence is thoroughly discussed. Suitable system parameters for the I-OFDR approach are defined: the system dynamic range and sensitivity are determined. A technique to suppress the dynamic range-limiting signal originating from strong reflections in the fibre is suggested. It is demonstrated that the I-OFDR technique has advantages over OTDR in terms of implementation for high-resolution measurement, measurement accuracy and signal stability. These advantages and measurement possibilities specific to the frequency domain approach are utilized for spatially resolved sensing applications in the second part of this work: A low optical loss polymer optical fibre (POF) is for the first time studied and analyzed for distributed strain sensing. The backscatter level dependence on strain in the fibre can be used to detect and locate strained fibre sections. Also, a correlation algorithm is proposed and demonstrated to measure length changes along the fibre with mm-resolution by correlating the typical backscatter signature of this fibre type. The fibre type is analyzed in detail regarding cross-sensitivities to temperature, relative humidity as well as mode propagation influences. The proposed sensing principles in combination with the highresolution I-OFDR allow for promising distributed sensing applications. Special interest is expressed by the structural health monitoring (SHM) sector since the fibre can measure strain values exceeding 100 %. Another sensing technique, specific to I-OFDR, is proposed for quasi-distributed and dynamic measurement of length changes and optical power changes at reflective events along the fibre. Precise calculation of the positions and reflected powers of multiple reflections can be conducted in parallel from the measurement of a few sampling points of the complex-valued frequency response. That allows for measuring with an increased repetition rate up to 2 kHz or at μm-scale length changes resolution at lower measurement frequencies. The approach is demonstrated in the laboratory and in a field application by measuring the deformation of a masonry building on a seismic shaking table. The I-OFDR exhibits competitive performance for general high-resolution backscatter measurement and the proposed optical fibre sensor principles may have promising prospects in the structural health monitoring (SHM) sector. T3 - BAM Dissertationsreihe - 125 KW - optical fiber sensor KW - OFDR KW - distributed backscatter measurement KW - polymer optical fiber (POF) sensor KW - structural health monitoring PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-4660 SN - 978-3-9816668-4-7 VL - 125 SP - 1 EP - 144 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-466 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 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-507508 DO - https://doi.org/10.1007/s00216-020-02687-5 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 - GEN A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 DO - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Liehr, Sascha T1 - ANNforPAT - Artificial Neural Networks for Process Analytical Technology N2 - This code accompanies the paper "Artificial neural networks for quantitative online NMR spectroscopy" published in Analytical and Bioanalytical Chemistry (2020). KW - Artificial neural networks KW - Automation KW - Online NMR spectroscopy KW - Process industry KW - Real-time process monitoring PY - 2020 UR - https://github.com/BAMresearch/ANNforPAT PB - GitHub CY - San Francisco AN - OPUS4-54481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Westerdicky, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are 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 developed 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-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible 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. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Online meeting DA - 01.02.2021 KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy PY - 2021 UR - www.date-conference.com SN - 978-3-9819263-5-4 SP - 615 EP - 620 PB - Research Publishing AN - OPUS4-52180 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -