2025
Jaufmann, R.; Widulle, N.; Ehrhardt, J.; Vranješ, D.; Niggemann, O.: On the Impact of Pretraining with Simulated Data on Anomaly Detection in CPS – A Case Study, 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025
Hohmann, M.; Yiming, A.; Penter, L.; Ihlenfeldt, S.; Niggemann, O.: A Data-Driven Approach for Automating the Design Process of Deep Drawing Tools, The 13th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Process (NUMISHEET), Munich, Germany, 2025
Ludwig, B.; Ehrhardt, J.; Niggemann, O.: Creating Virtual Sensors Using Neural Networks, 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025
Ludwig, B.; Maleshkova, M.; Niggemann, O.: CPSWatch: Lightweight Ontology for System Description and Diagnosis, 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, 2025
Moddemann, L.; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: The HAI-CPPS Benchmark: Evaluating AI Capabilities across Hybrid Data Spaces, 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Porto, Portugal, September 2025
Wittke, C.; Liebert, A.; Friesen, A.; Flatt, H.; Niggemann, O.: Potato-Glow: Utilizing Glow for Vision-Based Anomaly Detection in an Industrial Context: A Comparative Benchmarking Approach, Bildverarbeitung in der Automation (BVAu), Lemgo, Deutschland, 2024
Ludwigs, R.; Wittke, C.; Born, H.; Augustin, L.; Niggemann, O.; Kampker, A.: KI-basierte Partikelgrößenbestimmung in Suspensionen, Zeitschrift für wirtschaftlichen Fabrikbetrieb, 120(s1): 219–223, 2025, https://doi.org/10.1515/zwf-2024-0144
Wittke, C.; Ludwigs, R.; Tappe, M.; Schatz, M.; Kampker, A.; Niggemann, O.: Challenges and Opportunities in Developing INN-Based Control Systems for Modular Drones, Machine Learning for Cyber-Physical Systems – Proceedings of the ML4CPS 2025 Conference, Berlin, Deutschland: openHSU, 2025, pp. 69–78, https://doi.org/10.24405/20026
Zimmering, B.; Coelho, C.; Gupta, V.; Maleshkova, M.; Niggemann, O.: Breaking Free: Decoupling Forced Systems with Laplace Neural Networks, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Porto, Portugal, 2025
Niggemann, O.; Beyerer, J.; Fey, G.; Kampker, A.; Diedrich, A.; Kühnert, C.; Kritl, A.: ML4CPS 2025 Workshop Proceedings, 2025, https://doi.org/10.24405/20018
Heesch, R.; Eilermann, S.; Windmann, A.; Diedrich, A.; Rosenthal, P.; Niggemann, O.: Evaluating Large Language Models for Real-World Engineering Tasks, 2025, https://doi.org/10.48550/arXiv.2505.13484
Diedrich, A.; Kühnert, C.; Maier, G.; Schraven, J.; Niggemann, O.: AITwin: A Uniform Digital Twin Interface for Artificial Intelligence Applications, In: Schulz, D., Bauckhage, C. (eds) Informed Machine Learning. Cognitive Technologies, Springer, Cham., 2025, https://doi.org/10.1007/978-3-031-83097-6_3
Hohmann, M.; Yiming, A.; Penter, L.; Ihlenfeldt, S.; Niggemann, O.: An AI approach for predicting the active surface of deep drawing tools in try-out, at – Automatisierungstechnik, 73(4): 251-260, 2025, https://doi.org/10.1515/auto-2024-0130
Rosenthal, P.; Liebert, A.; Niggemann, O.: Finding optimal solution principles in conceptual design, 25th International Conference on Engineering Design (ICED), Dallas, USA, 2025
Roche, J.P.; Niggemann, O.; Friebe, J.: Using Autoencoders and Automatic Differentiation to Reconstruct Missing Variables in a Set of Time Series. Springer Nature Computer Science. 2025
2024
Schulz, M.; Fay, A.; Niggemann, O.; Matiaske, W.; Schulz, D.: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Band 2, DTEC.bw, 2024, https://doi.org/10.24405/16768
Diedrich, A.; Heesch, R.; Bozzano, M.; Ludwig, B.; Cimatti, A.; Niggemann, O.: Inferring Sensor Placement Using Critical Pairs and Satisfiability Modulo Theory, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Vranješ, D.; Ehrhardt, J.; Heesch, R.; Moddemann, L.; Steude, H.; Niggemann, O.: Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Heesch, R.; Cimatti, A.; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: Summary of A Lazy Approach to Neural Numerical Planning with Control Parameters, The 35th International Conference on Principles of Diagnosis and Resilient Systems, Vienna, Austria, November 2024
Diedrich, A.; Windmann, S.; Niggemann, O.: Solving industrial fault diagnosis problems with quantum computers, Quantum Mach. Intell. 6, 66, 2024, https://doi.org/10.1007/s42484-024-00184-x
Eilermann, S.; Lüddecke, L.; Hohmann, M.; Zimmering, B.; Oertel, M.; Niggemann, O.: A Neural Ordinary Differential Equations Approach for 2D Flow Properties Analysis of Hydraulic Structures, 1st ECAI Workshop on Machine Learning Meets Differential Equations: From Theory to Applications, Santiago de Compostela, Spain, October 2024
Zimmering, B.; Coelho, C.; Niggemann, O.: Optimising Neural Fractional Differential Equations for Performance and Efficiency, 1st ECAI Workshop on Machine Learning Meets Differential Equations: From Theory to Applications, Santiago de Compostela, Spain, October 2024
Liebert, A.; Dethof, F.; Keßler, S; Niggemann, O.: Automated Impact Echo Spectrum Anomaly Detection using U-Net Autoencoder, PAIS24, Santiago de Compostela, Spain, October 2024
Heesch, R.; Cimatti, A; Ehrhardt, J.; Diedrich, A.; Niggemann, O.: A Lazy Approach to Neural Numerical Planning with Control Parameters, 27TH European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, 2024
Boschmann, D.; Stieghorst, C; Knezevic, D; Kadri, L.; Niggemann, O.: Automation of PGAA Spectra Analysis with Deep Learning, 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024
Meyer, F.; Freitag, L.; Hinrichsen, S.; Niggemann, O.: Potentials of Large Language Models for Generating Assembly Instructions, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Widulle, N.; Meyer, F.; Niggemann, O.: Generating Assembly Instructions Using Reinforcement Learning in Combination with Large Language Models, 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024
Windmann, A.; Wittenberg, P.; Schieseck, M.; Niggemann, O.: Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems. 22nd IEEE International Conference on Industrial Informatics (INDIN), Beijing, China, 2024.
Moddemann, L.; Steude, H. S.; Diedrich, A.; Pill, I.; Niggemann, O.: Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Ehrhardt, J.; Overlöper, P; Vranjes, D; Steude, H; Diedrich, A.; Niggemann, O.: Using Modular Neural Networks for Anomaly Detection in Cyber-Physical Systems, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Ludwig, B.; Diedrich, A.; Niggemann, O.: Using Ontologies to Create Logical System Descriptions for Fault Diagnosis, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Overlöper, P.; Moddemann, L.; Hranisavljevic, N.; Windmann, A.; Niggemann, O.: Discretization of CPS Time Series with Neural Networks, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Zimmering, B.; Roche, J.P.; Niggemann, O.: Enhancing Nonlinear Electrical Circuit Modeling with Prior Knowledge-Infused Neural ODEs 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Hohmann, M.; Eilermann, S.; Großmann, W.; Niggemann, O.: Design Automation: A Conditional VAE Approach to 3D Object Generation under Conditions, 29th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Padova, Italy, 2024
Augustin, J.L.; Niggemann, O.: Self-Supervised Graph Structure Learning for Cyber-Physical Systems, ,12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Vranješ, D.; Niggemann, O.: Enhancing Cyber-Physical System Analysis with Structure-Aware Modular Neural Networks, 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), St. Louis, USA, 2024
Liebert, A.; Palani, A.; Rensmeyer, T.; Breuer, M.; Niggemann, O.: CNN-based Temperature Dynamics Approximation for Burning Rooms, SafeProcess24, Ferrara, Italy, June 2024
Steude, H. S.; Geier, C.; Moddemann, L.; Creutzenberg, M.; Pfeifer, J.; Turk, S.; Niggemann, O.: End-to-end MLOps Integration: A Case Study with ISS Telemetry Data, ML4CPS – Machine Learning for Cyber-Physical Systems, Berlin, Germany, 2024
Rosenthal, P.; Demke, N.; Mantwill, F.; Niggemann, O.: Plan-Based Derivation of General Functional Structures in Product Design, 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), St. Louis, USA, 2024
Zimmering, B.; Niggemann, O.: Integrating continuous-time neural networks in engineering: bridging machine learning and dynamical system modeling, Machine Learning for Cyber-Physical Systems (ML4CPS), Berlin, Germany, 2024, https://doi.org/10.24405/15313
Diedrich, A.; Moddemann, L.; Niggemann, O.: Learning System Descriptions for Cyber-Physical Systems, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Moddemann, L.; Steude, H.; Diedrich, A.; Niggemann, O.: Discret2Di – Deep Learning based Discretization for Model-based Diagnosis,12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Steude, H.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Niggemann, O.: Diagnosis driven Anomaly Detection for Cyber-Physical Systems, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Ferrara, Italy, 2024
Rensmeyer, T.; Großmann, W.; Kramer, D.; Niggemann, O.: Bayesian Transfer Learning of Neural Network-Based Interatomic Force Models, The 38th Annual AAAI Conference on Artificial Intelligence | Workshop on AI to Accelerate Science and Engineering, Vancouver, Canada, 2024
Merkelbach, S.; Diedrich, A.; von Enzberg, Sebastian; Niggemann, O.; Dumitrescu, R.: Towards the Generation of Models for Fault Diagnosis of CPS using VQA Models, Machine Learning for Cyber-Physical Systems (ML4CPS), Berlin, Germany, 2024
Großmann, W.; Eilermann, S.; Rensmeyer, T.; Liebert, A.; Hohmann, M; Wittke, C.; Niggemann, O.: Position Paper on Materials Design — A Modern Approach, The 38th Annual AAAI Conference on Artificial Intelligence | Workshop on AI to Accelerate Science and Engineering, Vancouver, Canada, 2024, DOI: 10.48550/arXiv.2312.10996
2023
Steude, H. S.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Niggemann, O.: Diagnosis Driven Anomaly Detection for CPS, Preprint, 2023
Petroll, C.; Eilermann, S.; Hofer, P.; Niggemann, O.: A Generative Neural Network Approach for 3D Multi-Criteria Design and Optimization of an Engine Mount for an Unmanned Air Vehicle, Preprint, 2023
Hinck, D; Schöttler, J.; Krantz, M.; Widulle, N.; Issleif, K.; Niggemann, O.: A next generation protective emblem: Cross-frequency protective options for non-combatants in the context of (fully) autonomous warfare, The International Review of the Red Cross, 2023
Eilermann, S.; Wehmeier, L.; Niggemann, O.; Deuter, A.: KIAAA: An AI Assistant for Teaching Programming in the Field of Automation, 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Germany, 2023, pp. 1-7, DOI: 10.1109/INDIN51400.2023.10218157
Moddemann, L.; Steude, H.; Niggemann, O.: Discret2Di – Deep Learning based Discretization for Model-based Diagnosis. 34th International Workshop on Principle of Diagnosis, Loma Mar, USA, 2023
Steude, H.; Moddemann, L.; Diedrich, A.; Ehrhardt, J.; Kalech, M.; Niggemann, O.: Diagnosis driven Anomaly Detection for CPS. 34th International Workshop on Principle of Diagnosis, Loma Mar, USA, 2023
Heesch, R.; Ehrhardt, J.; Niggemann, O.: Integrating Machine Learning into an SMT-based Planning Approach for Production Planning in Cyber-Physical Production Systems. International Workshop on Hybrid Models for Coupling Deductive and Inductive Reasoning (HYDRA), ECAI 2023 – European Conference on Artificial Intelligence, Krakau, Poland
Ehrhardt, J.; Heesch, R.; Niggemann, O.: Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems. International Workshop on Hybrid Models for Coupling Deductive and Inductive Reasoning (HYDRA), ECAI 2023 – European Conference on Artificial Intelligence, Krakau, Poland
Windmann, A.; Steude, H.; Niggemann, O.: Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study. Workshop of Artificial Intelligence for Time Series Analysis (AI4TS), IJCAI 2023 – International Joint Conference on Artificial Intelligence, Macao, China
Widulle, N.; Niggemann, O.: Using Reverse Reinforcement Learning for Assembly Tasks, PRL Workshop – Bridging the Gap Between AI Planning and Reinforcement Learning, IJCAI 2023 – International Joint Conference on Artificial Intelligence, Macao, China
Liebert, A.; Wittke, C.; Ehrhardt, J.; Jaufmann, R.; Widulle, N.; Eilermann, S.; Krantz, M.; Niggemann, O.: Using FliPSi to Generate Data for Machine Learning Algorithms, IEEE ETFA 2023 – IEEE International Conference on Emerging Technologies and Factory Automation, Siana, Romania
Krantz, M.; Widulle, N.; Niggemann, O.: Game Design Tools for ML Data Generation in CPS. In: 2023 9th International Conference on Automation, Robotics and Applications (ICARA), February 2023, to be published In: 2023 9th International Conference on Automation, Robotics and Applications (ICARA) Conference Proceedings, IEEE Xplore
Schöttler, J. J.; Bürklin, C.; Niggemann, O.: 54 Shapes of [naval] grey: AI based eo-sensor image classification of warships, NATO SET SET-311, NATO RESTRICTED, 2023
Schöttler, J. J.; Bürklin, C.; Niggemann, O.: Y inside: AI-based mapping of deinterleaved radar to a database, NATO SET SET-311, NATO RESTRICTED, 2023
Vieira da Silva, L.M.; Heesch, R.; Köcher, A.; Fay, A.: Transformation eines Fähigkeitsmodells in einen PDDL-Planungsansatz, at-Automatisierungstechnik 71.2, 2023, 105-115, DOI: 10.1515/auto-2022-0112
Niggemann, O.; Zimmering, B.; Steude, H.; Augustin, J.L.; Windmann, A.; Multaheb, S.: Machine Learning for Cyber-Physical Systems. In: Vogel-Heuser, B.; Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg, 2023
https://doi.org/10.1007/978-3-662-65004-2_17
Kiefer, B.; Kristan, M.; Perš, J.; Žust, L.; Poiesi, F.; Andrade, F.; Bernardino, A.; Dawkins, M.; Raitoharju, J.; Quan, Y.; Atmaca, A.; Hofer, T.; Zhang, Q.; Xu, Y.; Zhang, J.; Tao, D.; Sommer, L.; Spraul, R.; Zhao, H.; Zhang, H.; Zhao, Y.; Augustin, J.; Jeon, E.i.; Lee, I.; Zedda, L.; Loddo, A.; Di Ruberto, C.; Verma, S.; Gupta, S.; Muralidhara, S.; Hegde, N.; Xing, D.; Evangeliou, N.; Tzes, A.; Bartl, V.; Špaňhel, J.; Herout, A.; Bhowmik, N.; Breckon, T.; Kundargi, S.; Anvekar, T.; Tabib, R.; Mudenagudi, U.; Vats, A.; Song, Y.; Liu, D.; Li, Y.; Li, S.; Tan, C.; Lan, L.; Somers, V.; De Vleeschouwer, C.; Alahi, A.; Huang, H.W.; Yang, C.Y.; Hwang, J.N.; Kim, P.K.; Kim, K.; Lee, K.; Jiang, S.; Li, H.; Ziqiang, Z.; Vu, T.A.; Nguyen-Truong, H.; Yeung, S.K.; Jia, Z.; Yang, S.; Hsu, C.C.; Hou, X.Y.; Jhang, Y.A.; Yang, S.; & Yang, M.T: 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (pp. 265-302), 2023
2022
Roche, J.P.; Friebe, J.; Niggemann, O.: Knowledge Based Grey Box Modeling of Inaccessible Circuits for System EMC-Simulation in Time Domain, 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), Hannover, Germany, September 2022.
Multaheb, S.; Bauer, F.; Bretschneider, P.; Niggemann, O.: Learning physically meaningful representations of energy systems with variational autoencoders, 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022, Stuttgart, Germany, September 6 ‐ 9, 2022
Rensmeyer, T.; Multaheb, S.; Putzke, J.; Zimmering, B.; Niggemann, O.: Using domain-knowledge to improve machine learning: A survey of recent advances, atp Magazin 8/2022, Vulkan Verlag
Nordhausen, A.; Ehrhardt, J; Möller, N: LaiLa Modellfabrik-Eine Validierungsplattform für Künstliche Intelligenz im Bereich Cyber-Physischer Produktionssysteme im Leichtbau, In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 230-234, DOI: https://doi.org/10.24405/14522
Widulle, N.; Ehrhardt, J.; Krantz, M.; Liebert, A.; Nordhausen, A.; Niggemann, O.: Eine Simulationsumgebung für flexible Cyber-Physische Produktionssysteme zur Generierung realistischer Datensätze für maschinelle Lernverfahren, In: VDI-Berichte – Leitkongress Automation 2022 – Band 2399 · 2022, Seiten 533-545, ISSN: 0083-5560
Kelm, B.; Myschik, S.; Tappe, M.; Niggemann, O.: Anwendung eines modellbasierten Rekonfigurationsansatzes und Vorstellung eines Konzeptes zur qualitativen Systemüberwachung für das Lebenserhaltungssystems (ECLSS) des COLUMBUS-Moduls der ISS. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 117-122, DOI: https://doi.org/10.24405/14539
Ivanovic, Pavle; Schöttler, Jonas; Windmann, Alexander; Neumann, Philipp: SmartShip – AI-based Assistance Systems for the Maritime Sector. Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr 1 292-296, 2022
Widulle, N.; Vieira da Silva, L. M. ; Heesch, R. ; Putzke, J. ; Niggemann, O.: Investigating the Use of AI Planning Methods in Real-World CPS Use Cases. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 168-173, DOI: https://doi.org/10.24405/14547
Heesch, R.; Putzke, J.; Althoff, S.; Topalis, P.; Schieseck, M.; Fay, A.; Niggemann, O.: Anforderungen an eine Engineering-Plattform für die KI-basierte Automation. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 162-167, DOI: https://doi.org/10.24405/14546
Schieseck, S.; Topalis, P.; Heesch, R.; Putzke, J.; Fay, A.: Beschreibungsmittel für die modellbasierte KI-Entwicklung in Automatisierungssystemen. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 153-161, DOI: https://doi.org/10.24405/14545
Moddemann, L.; Steude, H.; Grashorn, P.; Niggemann, O.: Automated Anomaly Detection and Diagnosis of the Environmental Control System of the ISS. In: dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg: Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw – Band 1 · 2022, Seiten 123-128, DOI: https://doi.org/10.24405/14540
Krantz, M.; Widulle, N.; Nordhausen, A.; Liebert, A.; Ehrhardt, J.; Eilermann, S.; Niggemann, O.: FliPSi: Generating Data for the Training of Machine Learning Algorithms for CPPS. In Annual Conference of the PHM Society (Vol. 14, No. 1), Oktober 2022
Großmann, W.; Horn, H.; Niggemann, O.: Improving remote material classification ability with thermal imagery. Sci Rep 12, 17288, 2022, https://doi.org/10.1038/s41598-022-21588-4
Krantz, M.; Windmann, A.; Heesch, R.; Moddemann, L.; Niggemann, O.: “On a Uniform Causality Model for Industrial Automation”, arXiv:2209.09618, 2022
Kelm, Benjamin; Balzereit, Kaja; Moddemann, Lukas; Myschik, Stephan; Niggemann, Oliver: Application of a Model-based Reconfiguration Approach for the ISS Columbus Environmental Control and Life Support System (ECLSS), 33rd International Workshop on Principle of Diagnosis, Toulouse, France, 2022
Heesch, René; Widulle, Niklas; Köcher, Aljosha; Nordhausen, Anna; Vieira da Silva, Luis Miguel; Putzke, Julian; Niggemann, Oliver: Methoden der künstlichen Intelligenz für die automatisierte Planung von modularen Produktionsprozessen, Automation 2022, 2022
Vranješ, Daniel; Topalis, Philip; Niggemann, Oliver: Chancen und Herausforderungen für Künstliche Intelligenz in kleinen und mittelständischen Unternehmen, Automation 2022, 2022
Ehrhardt, Jonas; Ramonat, Malte; Heesch, René; Balzereit, Kaja; Diedrich, Alexander; Niggemann, Oliver: An AI benchmark for Diagnosis, Reconfiguration & Planning, 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2022), 2022
Schöttler, Jonas; Otto, Claus; Großmann, Willi; Niggemann, Oliver: Opportunities of AI for maritime forces in an in- and outward-looking view, ICMCIS2022, Elsevier Procedia, May 2022
Diedrich, Alexander; Niggemann, Oliver: On Residual-based Diagnosis of Physical Systems. Elsevier Engineering Applications of Artificial Intelligence, Volume 109, March 2022, 104636.
Vranješ, Daniel; Niggemann, Oliver: Anomaly detection based on time series data from industrial automatic sewing machines, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Liebert, Artur; Weber, Wolfgang; Reif, Sebastian; Niggemann, Oliver: Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Balzereit, Kaja; Niggemann, Oliver: AutoConf: A New Algorithm for Reconfiguration of Cyber-Physical Production Systems, IEEE Transactions on Industrial Informatics, January 2022
Köcher, Aljosha; Heesch, René; Widulle, Niklas; Nordhausen, Anna; Putzke, Julian; Windmann, Alexander; Niggemann, Oliver: A Research Agenda for AI Planning in the Field of Flexible Production Systems, The 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, England, 2022.
Steude, Henrik; Windmann, Alexander; Niggemann, Oliver: Learning Physical Concepts in Cyber-Physical Systems: A Case Study, The 11th IFAC Symposium on Fault Detection, Supervision, and Safety of Technical Processes (SAFEPROCESS 2022), Pafos, Cyprus, June 2022
2021
Dietrich, Alexander; Niggemann, Oliver: Diagnosing Systems through Approximated Information, Proceedings of the Annual Conference of the PHM Society, 2021.
Balzereit, Kaja; Niggemann, Oliver: Sound and Complete Reconfiguration for a Class of Hybrid Systems, 32nd International Workshop on Principle of Diagnosis, 2021, Hamburg, Germany
Roche, Jan-Philipp; Friebe, Jens; Niggemann, Oliver: Neural Network Modeling of Nonlinear Filters for EMC Simulation in Discrete Time Domain, 47th Annual Conference of the IEEE Industrial Electronics Society (IECON 2021), Toronto, Canada, 2021.
Rosenthal, Philipp; Niggemann, Oliver: Problem examination for AI methods in conceptual product design, IJCAI 2021 Workshop – AI and Product Design, Montreal, Canada, 2021.
Balzereit, Kaja; Diedrich, Alexander; Ginster, Jonas; Niggemann, Oliver: An Ensemble of Benchmarks for the Evaluation of AI Methods for Fault Handling in CPPS, IEEE International Conference on Industrial Informatics (INDIN 2021), Palma de Mallorca, Spain, 2021
Balzereit, Kaja; Niggemann, Oliver: Gradient-based Reconfiguration of Cyber-Physical Production Systems, IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021), Victoria, BC, Canada, 2021.
Zimmering, Bernd; Niggemann, Oliver; Hasterok, Constanze; Pfannstiel, Erik; Ramming, Dario; Pfrommer, Julius: Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods, Journal Sensors 2021, 21(7), 2397, https://doi.org/10.3390/s21072397
Niggemann, Oliver; Diedrich, Alexander, Pfannstiel, Erik; Schraven, Joshua; Kühnert, Christian: A Generic DigitalTwin Model for Artificial Intelligence Applications, IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Victoria, BC, Canada, May 2021
Multaheb, Samim; Zimmering, Bernd; Niggemann, Oliver: Expressing uncertainty in neural networks for production systems, at – Automatisierungstechnik 69(3):221-230, DOI: 10.1515/auto-2020-0122, March 2021
Book: Beyerer, Jürgen; Maier, Alexander; Niggemann, Oliver: Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2020: Selected papers from the International Conference ML4CPS 2020, January 2021
2020
Roche, Jan-Philipp; Friebe, Jens; Niggemann, Oliver: Machine Learning for Grey Box Modeling of Electric Components for Circuit- and EMC-Simulation, PCIM Europe Conference, 2020.
Niggemann, O., Biswas, G., Kinnebrew, J., Bunte, A., Hranisavljevic, N.: Handbuch Industrie 4.0 – Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion, Springer Verlag, 2020
Bunte, Andreas; Li, Peng Li; Niggemann: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems, Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, DOI: 10.1007/978-3-662-59084-3_6, Springer Vieweg, January 2020
Balzereit, Kaja; Niggemann, Oliver: Modeling Quantitative Effects for the Reconfiguration of Hybrid Systems, 31st International Workshop on Principles of Diagnosis DX, 2020
Diedrich, Alexander; Balzereit, Kaja; Niggemann, Oliver: First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems, Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, Springer Vieweg, January 2020
Balzereit, Kaja; Fullen, Marta; Niggemann, Oliver: A Concept for the Automated Reconfiguration of Quadcopters, Conference LWDA 2020, September 2020
Voß, Carlo; Eiteneuer, Benedikt; Niggemann, Oliver: Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems, 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Tampere, Finland, June 2020
Balzereit, Kaja; Niggemann, Oliver: Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories, 3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Tampere, Finland, June 2020
Li, Peng; Niggemann, Oliver: A Non-Convex Archetypal Analysis for One-class Classification based Anomaly Detection in Cyber-Physical Systems, IEEE Transactions on Industrial Informatics PP(99):1-1, DOI: 10.1109/TII.2020.3009106, July 2020
Hranisavljevic, Nemanja; Maier, Alexander; Niggemann, Oliver: Discretization of hybrid CPPS data into timed automaton using restricted Boltzmann machines, Engineering Applications of Artificial Intelligence 95:103826, DOI: 10.1016/j.engappai.2020.103826, August 2020
Giese, Katharina; Eickmeyer, Jens; Niggemann, Oliver: Differential Evolution in Production Process Optimization of Cyber Physical Systems, In book: Machine Learning for Cyber Physical Systems, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_3
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis, In book: Machine Learning for Cyber Physical Systems, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_7
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. In book: Machine Learning for Cyber Physical Systems – Selected papers from the International Conference ML4CPS, Springer Vieweg, January 2020, DOI:10.1007/978-3-662-59084-3_6
2019
Li, Peng; Niggemann, Oliver: Non-convex hull based anomaly detection in CPPS, Elsevier Engineering Applications of Artificial Intelligence 87, October 2019
Balzereit, Kaja; Niggemann, Oliver Niggemann: Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories, 30th International Workshop on Principles of Diagnosis (DX-19) November 2019
Bunte, Andreas; Wunderlich, Paul; Moriz, Natalia; Li, Peng; Mankowski, André, Rogalla, Antje; Niggemann, Oliver: Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS, IEEE Emerging Technologies and Factory Automation (ETFA), September 2019.
Bunte, Andreas; Fischbach, Andreas; Strohschein, Jan; Bartz-Beielstein, Thomas; Faeskorn-Woyke, Heide; Niggemann Oliver: Evaluation of Cognitive Architectures for Cyber-Physical Production Systems, 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sept. 2019.
Zhang, Fan; Pinkal, Kevin; Wefing, Patrick; Conradi, Florian; Schneider, Jan; Niggemann, Oliver: Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space. In: 20th IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia, Feb 2019.
Eiteneuer, Benedikt; Hranisavljevic, Nemanja; Niggemann, Oliver: Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder. In: 20th IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australien, Feb 2019.
Li, Peng; Niggemann, Oliver; Hammer, Barbara: On the Identification of Decision Boundaries for Anomaly Detection in CPPS. In: 20th IEEE International Conference on Industrial Technology (ICIT 2019) Melbourne, Australia, Feb 2019.
Diedrich, Alexander; Niggemann, Oliver: Model-based Diagnosis of Hybrid Systems using Satisfiability Modulo Theory. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019.
Bunte, Andreas; Stein, Benno; Niggemann, Oliver: Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019.
Windmann, S., Balzereit, K. & Niggemann, O: Model-based routing in flexible manufacturing systems. at – Automatisierungstechnik, 67(2), 2019
Balzereit, Kaja; Maier, Alexander; Björn, Barig, Niggemann, Oliver: Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems, 11th International Conference on Agents and Artificial Intelligence, Prague, Czech Republic, February 2019
2018
Li, Peng; Niggemann, Oliver; Hammer, Barbara: A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In: 44th Annual Conference of the IEEE Industrial Electronics Society (IECON) Oct 2018.
Li, Peng; Niggemann, Oliver: A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Rogalla, Antje; Fay, Alexander; Niggemann, Oliver: Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Bunte, Andreas; Niggemann, Oliver; Stein, Benno: Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018.
Wunderlich, Paul; Niggemann, Oliver: Inference Methods for Detecting the Root Cause of Alarm Floods in Causal Models. In: 23rd International Conference on Methods and Models in Automation and Robotics (MMAR) Międzyzdroje, Poland, Aug 2018.
Eiteneuer, Benedikt; Niggemann, Oliver: LSTM for model-based Anomaly Detection in Cyber-Physical Systems. In: Proceedings of the 29th International Workshop on Principles of Diagnosis Warsaw, Poland, Aug 2018.
von Birgelen, Alexander; Niggemann, Oliver: Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps. S.: 37-54, Springer Vieweg, Aug 2018.
von Birgelen, Alexander; Niggemann, Oliver: Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps. S.: 55-71, Springer Vieweg, Aug 2018.
Wunderlich, Paul; Niggemann, Oliver: Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause. In: IMPROVE – Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency: Intelligent Methods for the Factory of the Future S.: 111-129, Springer Vieweg, Aug 2018.
Windmann, Stefan; Niggemann, Oliver: A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. Springer Vieweg, Aug 2018.
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Validation of similarity measures for industrial alarm flood analysis. Springer Vieweg, Aug 2018.
Niggemann, Oliver; Schüller, Peter: IMPROVE – Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Springer Vieweg, Aug 2018.
Wunderlich, Paul; Niggemann, Oliver: Challenges in Learning Causal Models of Alarms in Industrial Plants. In: 16th IEEE International Conference on Industrial Informatics (INDIN) Porto, Portugal, Jul 2018.
Specht, Felix; Otto, Jens; Niggemann, Oliver; Hammer, Barbara: Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. In: IEEE 16th International Conference on Industrial Informatics (INDIN) Jul 2018.
Lang, Dorota; Wunderlich, Paul; Heinz, Mario; Wisniewski, Lukasz; Jasperneite, Jürgen; Niggemann, Oliver; Röcker, Carsten: Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS) Imperia (Italy), Jun 2018.
von Birgelen, Alexander; Buratti, Davide; Mager, Jens; Niggemann, Oliver: Self-Organizing Maps for Anomaly Localization and Predictive Maintenance in Cyber-Physical Production Systems. In: 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018) CIRP-CMS, May 2018.
Conradi, Florian; Wefing, Patrick; Pinkal, Kevin; Zhang, Fan; Niggemann, Oliver; Schneider, Jan: Inline progress measurement of the ß-amylase rest in the mashing process employing a near infrared transflectance probe. In: Trends in Brewing, Gent Apr 2018.
Wefing, Patrick; Conradi, Florian; Fuchs, Lukas; Schoppmeier, Jan; Pinkal, Kevin; Niggemann, Oliver; Schneider, Jan: Laboratory Plant for a closed loop-controlled continuous (CLCC) Mashing. In: Trends in Brewing, Gent Apr 2018.
Windmann, Stefan; Niggemann, Oliver; Stichweh, Heiko: Computation of energy efficient driving speeds in conveying systems. In: at – Automatisierungstechnik Mar 2018.
Nisic, Tatjana; Conradi, Florian; Niggemann, Oliver; Pinkal, Kevin; Schneider, Jan; Wefing, Patrick; Zhang, Fan: Food meets IT: der Digitale Zwilling erobert die Lebensmittelindustrie. In: IM+io Mar 2018.
Otto, Jens; Vogel-Heuser, Birgit; Niggemann, Oliver: Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber Physical Production Systems in the Domain of Discrete Manufacturing. In: IEEE Transactions on Industrial Informatics IEEE, Jan 2018.
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach. In: International Conference on Agents and Artificial Intelligence (ICAART) SCITEPRESS, Madeira, Portugal, Jan 2018.
2017
von Birgelen, Alexander; Niggemann, Oliver: Using Self-Organizing Maps to Learn Hybrid Timed Automata in Absence of Discrete Events. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017), Sep 2017.
Wunderlich, Paul; Niggemann, Oliver: Structure Learning Methods for Bayesian Networks to Reduce Alarm Floods by Identifying the Root Cause. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017), Sep 2017.
Rogalla, Antje; Niggemann, Oliver: Automated Process Planning for Cyber-Physical Production Systems. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2017.
Pethig, Florian; Niggemann, Oliver; Walter, Armin: Towards Industrie 4.0 Compliant Configuration of Condition Monitoring Services. In: 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Jul 2017.
Pinkal, Kevin; Niggemann, Oliver: A New Approach to Model-Based Test Case Generation for Industrial Automation Systems. In: 15th IEEE International Conference on Industrial Informatics (INDIN 2017), Jul 2017.
Fullen, Marta; Schüller, Peter; Niggemann, Oliver: Defining and validating similarity measures for industrial alarm flood analysis. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Jul 2017.
Bunte, Andreas; Li, Peng; Niggemann, Oliver: Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. In: 3rd Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS), October 2017.
Windmann, Stefan; Lang, Dorota; Niggemann, Oliver: Learning Parallel Automata of PLCs. In: 22nd IEEE International Conference on Emerging Technologies And Factory Automation, September 2017.
Windmann, Stefan; Niggemann, Oliver: A Self-Configurable Fault Detection System for Industrial Ethernet Networks. In: at – Automatisierungstechnik at – Automatisierungstechnik, May 2017.
Diedrich, Christian; Niggemann, Oliver; Pethig, Florian; Kraft, Andreas; Bock, Jürgen; Gössling, Andreas; Hänisch, Rolf; Reich, Johannes; Vollmar, Friedrich; Wende, Jörg: Interaktionsmodell für Industrie 4.0 Komponenten. In: At Automatisierungstechnik Jan 2017.
2016
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Semantics Enable Standardized User Interfaces for Diagnosis in Modular Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Denver, CO, USA, Oct 2016
Hranisavljevic, Nemanja; Niggemann, Oliver; Maier, Alexander: A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata. In: International Workshop on the Principles of Diagnosis (DX) Denver, Oct 2016
Diedrich, Alexander; Feldman, Alexander ; Perdomo-Ortiz, Alejandro ; Abreu, Rui ; Niggemann, Oliver; de Kleer, Johan: Applying Simulated Annealing to Problems in Model-based Diagnosis. In: International Workshop on the Principles of Diagnosis (DX) Denver, CO, USA, Oct 2016.
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Integrating Semantics for Diagnosis of Manufacturing Systems. In: 21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Berlin, Sep 2016.
Henning, Steffen; Niggemann, Oliver; Otto, Jens: Pattern-Based Control-Code Synthesis. In: 14th International IEEE Conference on Industrial Informatics (INDIN), Politiers (France) Jul 2016.
Bunte, Andreas; Diedrich, Alexander; Niggemann, Oliver: Integrating Semantics for Diagnosis of Manufacturing Systems. In: 21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Berlin, Sep 2016.
Windmann, Stefan; Niggemann, Oliver: A GPU-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. In: 14th International IEEE Conference on Industrial Informatics (INDIN), Poltiers (France), Jul 2016.
Li, Peng; Niggemann, Oliver: Improving Clustering Based Anomaly Detection with Concave Hull: An Application in Condition Monitoring of Wind Turbines. In: 14th IEEE International Conference on Industrial Informatics (INDIN 2016), Poltiers (France) Jul 2016.
Niggemann, Oliver; Biswas, Gautam; Khorasgani, Hamed; Volgmann, Sören; Bunte, Andreas: Datenanalyse in der intelligenten Fabrik. Springer Berlin Heidelberg, Berlin, Heidelberg, Jun 2016.
Maier, Alexander; Schriegel, Sebastian; Niggemann, Oliver: Big Data and Machine Learning for the Smart Factory – Solutions for Condition Monitoring, Diagnosis and Optimization. In: Industrial Internet of Things: Cybermanufacturing Systems, Springer Verlag, Jun 2016.
2015
Niggemann, Oliver; Frey, Christian: Data-Driven Anomaly Detection in Cyber-Physical Production Systems,, at – Automatisierungstechnik(63) S.: 821–832, Oct 2015
Vogel-Heuser, Birgit; Schütz, Daniel ; Schöler, Thorsten ; Pröll , Sebastian ; Jeschke, Sabina ; Ewert , Daniel ; Niggemann, Oliver; Windmann, Stefan; Berger, Ulrich ; Lehmann, Christian : Agentenbasierte cyber-physische Produktionssysteme – Anwendungen für Industrie 4.0. In: atp edition, DIV Vulkan-Verlag, München, Sep 2015.
Shrestha, Ganesh Man; Niggemann, Oliver: Hybrid Approach Combining Bayesian Network and Rule-based Systems for Resource Optimization in Industrial Cleaning Processes. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Windmann, Stefan; Jungbluth, Florian ; Niggemann, Oliver: A HMM-Based Fault Detection Method for Piecewise Stationary Industrial Processes. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Windmann, Stefan; Niggemann, Oliver: MapReduce Algorithms for Efficient Generation of CPS Models from Large Historical Data Sets. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg, Sep 2015.
Specht, Felix; Flatt, Holger; Eickmeyer, Jens; Niggemann, Oliver: Exploiting Multicore Processors in PLCs using Libraries for IEC 61131-3. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) Luxembourg , Sep 2015.
Eickmeyer, Jens; Li, Peng; Pethig, Florian; Niggemann, Oliver: Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants. In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Maier, Alexander; Niggemann, Oliver: On the Learning of Timing Behavior for Anomaly Detection in Cyber-Physical Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Niggemann, Oliver; Biswas, Gautam; Kinnebrew, John S.; Khorasgani, Hamed; Volgmann, Sören; Bunte, Andreas: Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control. In: International Workshop on the Principles of Diagnosis (DX); Paris, France In: International Workshop on the Principles of Diagnosis (DX) Paris, France, Aug 2015.
Windmann, Stefan; Niggemann, Oliver: Efficient Fault Detection for Industrial Automation Processes with Observable Process Variables. In: IEEE International Conference on Industrial Informatics (INDIN 2015) Cambridge, UK, Jul 2015.
Windmann, Stefan; Niggemann, Oliver; Stichweh, Heiko: Energy efficiency optimization by automatic coordination of motor speeds in conveying systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) Mar 2015.
Windmann, Stefan; Niggemann, Oliver: Automatic model separation and application to diagnosis in industrial automation systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) Mar 2015.
Lohweg, Volker; Niggemann, Oliver: On the Diagnosis of Cyber-Physical Production Systems – State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15) Austin, Texas, USA, Jan 2015.
2014
Henning, Steffen; Otto, Jens; Niggemann, Oliver; Schriegel, Sebastian: A Descriptive Engineering Approach for Cyber-Physical Systems. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Spain, Sep 2014.
Niggemann, Oliver; Kroll, Bjoern: On the applicability of model based software development to cyber physical production systems. In: CyPhERS 2nd Experts Workshop CPSWeek 2014 CPSWeek 2014, Berlin, Germany, April 14 2014 , Apr 2014.
Kroll, Bjoern; Schaffranek, David; Schriegel, Sebastian; Niggemann, Oliver: System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2014.
Niggemann, Oliver; Windmann, Stefan; Volgmann, Sören; Bunte, Andreas; Stein, Benno: Using Learned Models for the Root Cause Analysis of Cyber-Physical Production Systems. In: International Workshop on the Principles of Diagnosis (DX) Graz, Austria, Sep 2014.
Shrestha, Ganesh Man; Niggemann, Oliver: A Bayesian Predictive Assistance System for Resource Optimization – A Case Study in Industrial Cleaning Process. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Sep 2014.
Moriz, Natalia; Böttcher, Björn; Niggemann, Oliver; Lackhove, Josef: Assisted Design for Automation Systems – from Formal Requirements to Final Designs. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Spain, Sep 2014.
Frey, Christian; Heinzmann, Michael; Jasperneite, Jürgen; Niggemann, Oliver; Sauer, Olaf; Schleipen, Miriam; Usländer, Thomas: IKT in der Fabrik der Zukunft – Ein Diskussionsbeitrag zu Industrie 4.0. In: atp edition DIV Vulkan Verlag, München, Aug 2014.
Volgmann, Sören; Rangel, Francisco; Niggemann, Oliver; Rosso, Paolo: Emotional Trends in Social Media – A State Space Approach. In: 21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014 S.: Vol. 263, pp. 1123-1124, IOS Press, Aug 2014.
Böttcher, Björn; Moriz, Natalia; Niggemann, Oliver: From Formal Requirements on Technical Systems to Complete Designs – A Holistic Approach. In: 21st European Conference on Artificial Intelligence (ECAI 2014) In: 21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014 S.: pp. 977-978, Vol. 263, Prague, Czech Republic, Aug 2014.
Niggemann, Oliver; Jasperneite, Jürgen: Konzepte und Anwendungsfälle für die intelligente Fabrik. In: Bauernhansl, Thomas; ten Hompel, Michael; Vogel-Heuser, Birgit (Hrsg.): Industrie 4.0 in Produktion, Automatisierung und Logistik Springer-Verlag, Jun 2014.
Anis, Anas; Schäfer, Wilhelm; Niggemann, Oliver: A Comparison of Modeling Approaches for Planning in Cyber Physical Production Systems. In: 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona, Jun 2014.
Niggemann, Oliver: Industrie 4.0 ohne modellbasierte Softwareentwicklung – Und warum es ohne Modelle nicht gehen wird. In: atp edition(Ausgabe 05 ) May 2014.
Windmann, Stefan; Niggemann, Oliver: Intelligente Assistenzsysteme für die Automation – Menschen bei der Prozessführung besser unterstützen. In: atp edition – Ausgabe 04 2014 Apr 2014.
Frey, Christian; Heinzmann, Michael; Jasperneite, Jürgen; Niggemann, Oliver; Sauer, Olaf; Schleipen, Miriam; Usländer, Thomas; Voit, Michael: IKT in der Fabrik der Zukunft – Ein Diskussionsbeitrag zu Industrie 4.0. In: atp edition Mar 2014.
2013
Niggemann, Oliver; Vodenčarević, Asmir; Maier, Alexander; Windmann, Stefan; Kleine Büning, Hans: A Learning Anomaly Detection Algorithm for Hybrid Manufacturing Systems. In: The 24th International Workshop on Principles of Diagnosis (DX-2013) Jerusalem, Israel, Oct 2013.
Kroll, Bjoern; Schriegel, Sebastian; Niggemann, Oliver: A Software Architecture for the Analysis of Energy- and Process-Data. In: 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2013.
Böttcher, Björn; Badinger, Johann; Moriz, Natalia; Niggemann, Oliver: Design of Industrial Automation Systems – Formal Requirements in the Engineering Process. In: 18thIEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Cagliari, Italy, Sep 2013.
Gilani, Syed Sheraz; Windmann, Stefan; Pethig, Florian; Kroll, Bjoern; Niggemann, Oliver: The Importance of Model-Learning for the Analysis of the Energy Consumption of Production Plant. In: 18th IEEE International Conference on Emerging Technologies & Factory Automation (ETFA), Cagliari, Italy Sep 2013.
Windmann, Stefan; Jiao, Shuo; Niggemann, Oliver; Borcherding, Holger: A Stochastic Method for the Detection of Anomalous Energy Consumption in Hybrid Industrial Systems. In: 11th International IEEE Conference on Industrial Informatics 2013 Bochum, Germany, May 2013.
Faltinski, Sebastian ; Jäger, Michael; Niggemann, Oliver; Marek, Frank: Auf dem Weg vom Spielzeug zum Werkzeug. In: atp edition May 2013.
Schetinin, Nikolai; Moriz, Natalia; Kumar, Barath; Faltinski, Sebastian ; Niggemann, Oliver; Maier, Alexander: Why do verification approaches in automation rarely use HIL-test? In: IEEE International Conference on Industrial Technology (ICIT) 25.-27. February 2013, Cape Town, South Africa, Feb 2013.
Maier, Alexander; Köster, Markus; Paiz Gatica, Carlos; Niggemann, Oliver: Automated Generation of Timing Models in Distributed Production Plants. In: IEEE International Conference on Industrial Technology (ICIT 2013), Cape Town, South Africa, Feb 2013 Feb 2013.
2012
Pethig, Florian; Kroll, Bjoern; Niggemann, Oliver; Maier, Alexander; Tack, Tim: A Generic Synchronized Data Acquisition Solution for Distributed Automation Systems. In: 18th IEEE International Conference on Emerging Technologies and Factory Automation Krakow, Poland, Sep 2012.
Jasperneite, Jürgen; Niggemann, Oliver: Intelligente Assistenzsysteme zur Beherrschung der Systemkomplexität in der Automation. In: ATP edition – Automatisierungstechnische Praxis 9/2012 Oldenbourg Verlag, München, Sep 2012.
Niggemann, Oliver; Stein, Benno; Vodenčarević, Asmir; Maier, Alexander; Kleine Büning, Hans: Learning Behavior Models for Hybrid Timed Systems. In: Twenty-Sixth Conference on Artificial Intelligence (AAAI-12) Jul 2012.
Faltinski, Sebastian ; Flatt, Holger; Pethig, Florian; Kroll, Bjoern; Vodenčarević, Asmir; Maier, Alexander; Niggemann, Oliver: Detecting Anomalous Energy Consumptions in Distributed Manufacturing Systems. In: IEEE 10th International Conference on Industrial Informatics (INDIN), 2012 Beijing, China, Jul 2012.
Faltinski, Sebastian ; Niggemann, Oliver; Moriz, Natalia; Mankowski, Andre: AutomationML: From Data Exchange to System Planning and Simulation.. In: 2012 IEEE International Conference on Industrial Technology (ICIT) Athen, Griechenland, Mar 2012.
2011
Vodenčarević, Asmir; Kleine Büning, Hans; Niggemann, Oliver; Maier, Alexander: Identifying Behavior Models for Process Plants. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation ETFA’2011, Toulouse, France, 2011 In: 16th IEEE International Conference on Emerging Technologies & Factory Automation (ETFA) Sep 2011.
Jäger, Michael; Just, Roman; Niggemann, Oliver: Using automatic Topology Discovery to diagnose PROFINET networks. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011) Toulouse, France, Sep 2011.
Wienke, Michael; Faltinski, Sebastian Niggemann, Oliver; Jasperneite, Jürgen: mINA-DL: A Novel Description Language Enabling Dynamic Reconfiguration in Industrial Automation. In: 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011) Toulouse, France, Sep 2011.
2010
Niggemann, Oliver; Lohweg, Volker; Tack, Tim: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010) Sep 2010.
Niggemann, Oliver: System-Level Design and Simulation of Automation Systems. In: 8th IEEE International Workshop on Factory Communication Systems – COMMUNICATION in AUTOMATION (WFCS 2010) May 2010.
2009
Kumar, Barath; Niggemann, Oliver; Jasperneite, Jürgen: Timed Automata for Modeling Network Traffic. In: Machine Learning in Real-Time Applications (MLRTA 09) (in conjunction with 32nd Annual Conference on Artificial Intelligence (KI 2009)) Paderborn, Germany, Sep 2009.
Niggemann, Oliver et al.: Using Simulation to Verify Diagnosis Algorithms of Electronic Systems. To be published in SAE World Congress, 2009. , Mar 2009.
2008
Niggemann, Oliver et al.: Durchgängige Systemtests von der virtuellen Integration bis zum Verbundtest.. In: ATZ elektronik Nov 2008.
Stichling, Dirk; Niggemann, Oliver; Fleischer, Dirk: Combining Automotive System and Function Models to support Code Generation and Early System Verification. Convergence 2008, October 20-22, 2008, Detroit, Michigan, USA , Oct 2008.
Otterbach, Rainer; Niggemann, Oliver; Fleischer, Dirk; Jogi, Santhosh: Using Software Architecture Models in Automotive Development Processes. SAE 2008 Commercial Vehicle Engineering Congress, October 7-9, 2008, Rosemont, Illinois, USA, Oct 2008.
Niggemann, Oliver; Stroop, Joachim: Models for Model’s Sake. Proceedings of the 30th International Conference on Software Engineering (ICSE) – Experience Track on Automotive Systems, Leipzig, Germany, 10 – 18 May 2008 , May 2008.
2007
Niggemann, Oliver; Eisemann, Ulrich; Beine, Michael; Kiffmeier, Ulrich: Behavior Modeling Tools in an Architecture-Driven Development Process – From Function Models to AUTOSAR. SAE World Congress & Exhibition, April 2007, Detroit, USA , Apr 2007.
2006
Stein, Benno; Niggemann, Oliver; Balzer, Heinrich: Diagnosis in Automotive Applications. 3rd Monet Workshop on Model-Based Systems (MBS-06) at the ECAI 06 at Riva de Gard, Italy, 2006 , Aug 2006.
Stein, Benno; Niggemann, Oliver; Lettmann, Theodor: Speeding up Model-based Diagnosis by a Heuristic Approach to Solving SAT. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 06), Innsbruck, Austria, Anaheim, Calgary, Zurich, February 2006. , Feb 2006.
2002
Bach, Roland; Zeller, Vitus; Cheleg, Alexei; Niggemann, Oliver: Assessing the Influence of Linear/Nonlinear Effects on Different Q-Factor Measurement Methods. 18th National Fiber Optic Engineer Conference (NFOEC), Dallas, USA, 2002 , Jul 2002.
2001
Niggemann, Oliver; Stein, Benno: Generation of Similarity Measures from Different Sources. The Fourteenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-2001). Springer-Verlag (Lecture Notes in the Computer Science/Lecture Notes in Artificial Intelligence), 2001 , Sep 2001.
Niggemann, Oliver; Stein, Benno; Tölle, Jens: Visualization of Traffic Structures. IEEE International Conference on Communications (ICC) 2001, Helsinki, Finland , Aug 2001.
Niggemann, Oliver: Visual Data Mining of Graph-Based Data. University of Paderborn, 2001. An electronic copy is available from the library of the University of Paderborn (http://ubdata.uni-paderborn.de/ediss/17/2001/niggeman/). , Jun 2001.
Lappe, Michael; Parl, Jong; Niggemann, Oliver; Holm, Liisa: Generating Protein Interaction Maps from Incomplete Data: Application to Fold Assignment. „9th International Conference on Intelligent Systems for Molecular Biology“. „9th International Conference on Intelligent Systems for Molecular Biology“, Copenhagen, Denmark, 2001 , Mar 2001.
1997-2000
Niggemann, Oliver; Stein, Benno: Learning the optimal Graph-Drawing Method for clustered Graphs. In Advanced Visual Interfaces 2000, ACM , Jul 2000.
B. Stein and O. Niggemann. On the nature of structure and its identification. In 25th International Workshop on Graph-Theoretic Concepts in Computer Science, Lecture Notes In Computer Science, Springer Verlag, 1999
Oliver Niggemann, Benno Stein and Michael Suermann. Network Configuration: Approaches for Solving the Cable Management Problem. International Symposium on Mathematical Programming (ISMP 97). Swiss Federal Institute of Technology, Lausanne (EPFL), August 1997.
Letzte Änderung: 19. Juni 2025