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ORCIDOliver Niggemann
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- affiliation: Hochschule Ostwestfalen-Lippe, Lemgo, Germany
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2020 – today
- 2023
[j16]Kaja Balzereit
, Oliver Niggemann:
AutoConf: New Algorithm for Reconfiguration of Cyber-Physical Production Systems. IEEE Trans. Ind. Informatics 19(1): 739-749 (2023)- 2022
- [j15]Alexander Diedrich, Oliver Niggemann:
On Residual-based Diagnosis of Physical Systems. Eng. Appl. Artif. Intell. 109: 104636 (2022) - [c114]Jonas Ehrhardt, Malte Ramonat, René Heesch, Kaja Balzereit, Alexander Diedrich, Oliver Niggemann:
An AI benchmark for Diagnosis, Reconfiguration & Planning. ETFA 2022: 1-8 - [c113]Samim Ahmad Multaheb, Fabian Bauer, Peter Bretschneider, Oliver Niggemann:
Learning Physically Meaningful Representations of Energy Systems with Variational Autoencoders. ETFA 2022: 1-6 - [c112]Aljosha Köcher, René Heesch, Niklas Widulle, Anna Nordhausen, Julian Putzke, Alexander Windmann, Oliver Niggemann:
A Research Agenda for AI Planning in the Field of Flexible Production Systems. ICPS 2022: 1-8 - [c111]Artur Liebert, Wolfgang Weber, Sebastian Reif, Bernd Zimmering, Oliver Niggemann:
Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions. ICPS 2022: 1-8 - [c110]Daniel Vranjes, Oliver Niggemann:
Anomaly detection based on time series data from industrial automatic sewing machines. ICPS 2022: 1-8 - [i10]Philipp Rosenthal, Oliver Niggemann:
Problem examination for AI methods in product design. CoRR abs/2201.07642 (2022) - [i9]Maria Krantz, Alexander Windmann, René Heesch, Lukas Moddemann, Oliver Niggemann:
On a Uniform Causality Model for Industrial Automation. CoRR abs/2209.09618 (2022) - 2021
- [j14]Samim Ahmad Multaheb, Bernd Zimmering, Oliver Niggemann:
Expressing uncertainty in neural networks for production systems. Autom. 69(3): 221-230 (2021) - [j13]Bernd Zimmering, Oliver Niggemann, Constanze Hasterok, Erik Pfannstiel, Dario Ramming, Julius Pfrommer:
Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods. Sensors 21(7): 2397 (2021) - [j12]Peng Li, Oliver Niggemann:
A Nonconvex Archetypal Analysis for One-Class Classification Based Anomaly Detection in Cyber-Physical Systems. IEEE Trans. Ind. Informatics 17(9): 6429-6437 (2021) - [c109]Oliver Niggemann, Alexander Diedrich, Christian Kühnert, Erik Pfannstiel, Joshua Schraven:
A Generic DigitalTwin Model for Artificial Intelligence Applications. ICPS 2021: 55-62 - [c108]Kaja Balzereit, Oliver Niggemann:
Gradient-based Reconfiguration of Cyber-Physical Production Systems. ICPS 2021: 125-131 - [c107]Jan-Philipp Roche, Jens Friebe, Oliver Niggemann:
Neural Network Modeling of Nonlinear Filters for EMC Simulation in Discrete Time Domain. IECON 2021: 1-7 - [c106]Kaja Balzereit, Alexander Diedrich, Jonas Ginster, Stefan Windmann, Oliver Niggemann:
An Ensemble of Benchmarks for the Evaluation of AI Methods for Fault Handling in CPPS. INDIN 2021: 1-6 - [i8]Kaja Balzereit, Oliver Niggemann:
Reconfiguring Hybrid Systems Using SAT. CoRR abs/2105.08398 (2021) - [i7]Henrik S. Steude, Alexander Windmann, Oliver Niggemann:
Learning Physical Concepts in Cyber-Physical Systems: A Case Study. CoRR abs/2111.14151 (2021) - [i6]Aljosha Köcher, René Heesch, Niklas Widulle, Anna Nordhausen, Julian Putzke, Alexander Windmann, Sven Vagt, Oliver Niggemann:
A Research Agenda for Artificial Intelligence in the Field of Flexible Production Systems. CoRR abs/2112.15484 (2021) - 2020
- [j11]Peng Li, Oliver Niggemann:
Non-convex hull based anomaly detection in CPPS. Eng. Appl. Artif. Intell. 87 (2020) - [j10]Nemanja Hranisavljevic, Alexander Maier, Oliver Niggemann:
Discretization of hybrid CPPS data into timed automaton using restricted Boltzmann machines. Eng. Appl. Artif. Intell. 95: 103826 (2020) - [c105]Kaja Balzereit, Oliver Niggemann:
Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories. ICPS 2020: 461-468 - [c104]Carlo Voß, Benedikt Eiteneuer, Oliver Niggemann:
Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems. ICPS 2020: 475-480 - [c103]Kaja Balzereit, Marta Fullen, Oliver Niggemann:
A Concept for the Automated Reconfiguration of Quadcopters. LWDA 2020: 180-191 - [e4]Jürgen Beyerer, Alexander Maier, Oliver Niggemann:
Machine Learning for Cyber Physical Systems, Selected papers from the International Conference ML4CPS 2017, Lemgo, Germany, September 25-26, 2017. Springer 2020, ISBN 978-3-662-59083-6 [contents] - [i5]Oliver Niggemann, Alexander Diedrich, Christian Kühnert, Erik Pfannstiel, Joshua Schraven:
The DigitalTwin from an Artificial Intelligence Perspective. CoRR abs/2010.14376 (2020) - [i4]Benedikt Eiteneuer, Nemanja Hranisavljevic, Oliver Niggemann:
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder. CoRR abs/2010.14957 (2020) - [i3]Nemanja Hranisavljevic, Oliver Niggemann, Alexander Maier:
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata. CoRR abs/2010.15415 (2020) - [i2]Benedikt Eiteneuer, Oliver Niggemann:
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems. CoRR abs/2010.15680 (2020)
2010 – 2019
- 2019
- [j9]Stefan Windmann, Kaja Balzereit, Oliver Niggemann:
Model-based routing in flexible manufacturing systems. Autom. 67(2): 95-112 (2019) - [c102]Alexander Diedrich, Alexander Maier, Oliver Niggemann:
Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory. AAAI 2019: 1452-1459 - [c101]Andreas Bunte, Benno Stein, Oliver Niggemann:
Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. AAAI 2019: 2727-2735 - [c100]Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, Oliver Niggemann:
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. ETFA 2019: 729-736 - [c99]Andreas Bunte, Paul Wunderlich, Natalia Moriz, Peng Li, André Mankowski, Antje Rogalla, Oliver Niggemann:
Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS. ETFA 2019: 1701-1704 - [c98]Kaja Balzereit, Alexander Maier, Björn Barig, Tino Hutschenreuther, Oliver Niggemann:
Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems. ICAART (2) 2019: 592-601 - [c97]Benedikt Eiteneuer, Nemanja Hranisavljevic, Oliver Niggemann:
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder. ICIT 2019: 1286-1292 - [c96]Peng Li, Oliver Niggemann, Barbara Hammer:
On the Identification of Decision Boundaries for Anomaly Detection in CPPS. ICIT 2019: 1311-1316 - [c95]Fan Zhang, Kevin Pinkal, Patrick Wefing, Florian Conradi, Jan Schneider, Oliver Niggemann:
Quality Control of Continuous Wort Production through Production Data Analysis in Latent Space. ICIT 2019: 1323-1328 - [e3]Jürgen Beyerer, Christian Kühnert, Oliver Niggemann:
Machine Learning for Cyber Physical Systems, Selected papers from the International Conference ML4CPS 2018, Karlsruhe, Germany, October 23-24, 2018. Springer 2019, ISBN 978-3-662-58484-2 [contents] - [i1]Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, Oliver Niggemann:
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. CoRR abs/1902.08448 (2019) - 2018
- [j8]Jürgen Beyerer, Oliver Niggemann:
Machine Learning in Automation. Autom. 66(4): 281-282 (2018) - [j7]Stefan Windmann, Oliver Niggemann, Heiko Stichweh:
Computation of energy efficient driving speeds in conveying systems. Autom. 66(4): 308-319 (2018) - [j6]Jens Otto, Birgit Vogel-Heuser, Oliver Niggemann:
Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques. Autom. 66(4): 331-343 (2018) - [j5]Jens Otto, Birgit Vogel-Heuser, Oliver Niggemann:
Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber-Physical Production Systems in the Domain of Discrete Manufacturing. IEEE Trans. Ind. Informatics 14(1): 275-282 (2018) - [c94]Antje Rogalla, Alexander Fay, Oliver Niggemann:
Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing. ETFA 2018: 464-471 - [c93]Stefan Windmann, Oliver Niggemann:
Information Retrieval in Industrial Production Environments. ETFA 2018: 1205-1208 - [c92]Peng Li, Oliver Niggemann:
A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0. ETFA 2018: 1375-1382 - [c91]Andreas Bunte, Oliver Niggemann, Benno Stein:
Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA. ETFA 2018: 1383-1390 - [c90]Andreas Bunte, Peng Li, Oliver Niggemann:
Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach. ICAART (2) 2018: 430-437 - [c89]Peng Li, Oliver Niggemann, Barbara Hammer:
A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. IECON 2018: 5345-5352 - [c88]Paul Wunderlich, Oliver Niggemann:
Challenges in Learning Causal Models of Alarms in Industrial Plants. INDIN 2018: 623-628 - [c87]Felix Specht, Jens Otto, Oliver Niggemann, Barbara Hammer:
Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems. INDIN 2018: 760-765 - [c86]Paul Wunderlich, Oliver Niggemann:
Inference Methods for Detecting the Root Cause of Alarm Floods in Causal Models. MMAR 2018: 893-898 - [c85]Alexander Diedrich, Oliver Niggemann:
Diagnosing Hybrid Cyber-Physical Systems using State-Space Models and Satisfiability Modulo Theory. DX 2018 - [c84]Benedikt Eiteneuer, Oliver Niggemann:
LSTM for Model-based Anomaly Detection in Cyber-Physical Systems. DX 2018 - [c83]Dorota Lang, Paul Wunderlich, Mario Heinz, Lukasz Wisniewski, Jürgen Jasperneite, Oliver Niggemann, Carsten Röcker:
Assistance system to support troubleshooting of complex industrial systems. WFCS 2018: 1-4 - 2017
- [j4]Christian Diedrich, Alexander Bieliaiev, Jürgen Bock, Andreas Gössling, Rolf Hänisch, Andreas Kraft, Florian Pethig, Oliver Niggemann, Johannes Reich, Friedrich Vollmar, Jörg Wende:
Interaktionsmodell für Industrie 4.0 Komponenten. Autom. 65(1): 5-18 (2017) - [j3]Stefan Windmann, Oliver Niggemann:
A self-configurable fault detection system for Industrial Ethernet networks. Autom. 65(6): 396 (2017) - [c82]Sebastian Büttner, Paul Wunderlich, Mario Heinz, Oliver Niggemann, Carsten Röcker:
Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. CD-MAKE 2017: 69-82 - [c81]Alexander von Birgelen, Oliver Niggemann:
Using self-organizing maps to learn hybrid timed automata in absence of discrete events. ETFA 2017: 1-8 - [c80]Christian Diedrich, Alexander Belyaev, Tizian Schroder, Jens Vialkowitsch, Alexander Willmann, Thomas Usländer, Heiko Koziolek, Jörg Wende, Florian Pethig, Oliver Niggemann:
Semantic interoperability for asset communication within smart factories. ETFA 2017: 1-8 - [c79]Antje Rogalla, Oliver Niggemann:
Automated process planning for cyber-physical production systems. ETFA 2017: 1-8 - [c78]Stefan Windmann, Dorota Lang, Oliver Niggemann:
Learning parallel automata of PLCs. ETFA 2017: 1-7 - [c77]Stefan Windmann, Oliver Niggemann, Holger Ruwe, Friedrich Becker:
A novel self-configuration method for RFID systems in industrial production environments. ETFA 2017: 1-5 - [c76]Paul Wunderlich, Oliver Niggemann:
Structure learning methods for Bayesian networks to reduce alarm floods by identifying the root cause. ETFA 2017: 1-8 - [c75]Kevin Pinkal, Oliver Niggemann:
A new approach to model-based test case generation for industrial automation systems. INDIN 2017: 53-58 - [c74]Florian Pethig, Oliver Niggemann, Armin Walter:
Towards Industrie 4.0 compliant configuration of condition monitoring services. INDIN 2017: 271-276 - [c73]Marta Fullen, Peter Schüller, Oliver Niggemann:
Defining and validating similarity measures for industrial alarm flood analysis. INDIN 2017: 781-786 - [c72]Katharina Giese, Jens Eickmeyer, Oliver Niggemann:
Differential Evolution in Production Process Optimization of Cyber Physical Systems. ML4CPS 2017: 17-23 - [c71]Andreas Bunte, Peng Li, Oliver Niggemann:
Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. ML4CPS 2017: 43-51 - [c70]Marta Fullen, Peter Schüller, Oliver Niggemann:
Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis. ML4CPS 2017: 53-61 - [p1]Oliver Niggemann, Gautam Biswas, John S. Kinnebrew, Hamed Khorasgani, Sören Volgmann, Andreas Bunte:
Datenanalyse in der intelligenten Fabrik. Handbuch Industrie 4.0 (2) 2017: 471-490 - [e2]Jürgen Beyerer, Oliver Niggemann, Christian Kühnert:
Machine Learning for Cyber Physical Systems, Selected papers from the International Conference ML4CPS 2016, Karlsruhe, Germany, September 29, 2016. Springer 2017, ISBN 978-3-662-53805-0 [contents] - 2016
- [c69]Andreas Bunte, Alexander Diedrich, Oliver Niggemann:
Integrating semantics for diagnosis of manufacturing systems. ETFA 2016: 1-8 - [c68]Alexander Diedrich, Björn Böttcher, Oliver Niggemann:
Exposing Design Mistakes During Requirements Engineering by Solving Constraint Satisfaction Problems to Obtain Minimum Correction Subsets. ICAART (2) 2016: 280-287 - [c67]Stefan Windmann, Oliver Niggemann:
A GPU-based method for robust and efficient fault detection in industrial automation processes. INDIN 2016: 442-445 - [c66]Peng Li, Oliver Niggemann:
Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines. INDIN 2016: 463-466 - [c65]Jens Otto, Birgit Vogel-Heuser, Oliver Niggemann:
Optimizing modular and reconfigurable cyber-physical production systems by determining parameters automatically. INDIN 2016: 1100-1105 - [c64]Steffen Henning, Jens Otto, Oliver Niggemann:
Pattern-based control-code synthesis. INDIN 2016: 1106-1111 - [e1]Oliver Niggemann, Jürgen Beyerer:
Machine Learning for Cyber Physical Systems, Selected papers from the International Conference ML4CPS 2015, Lemgo, Germany, October 1-2, 2015. Springer 2016, ISBN 978-3-662-48836-2 [contents] - 2015
- [j2]Oliver Niggemann, Christian W. Frey:
Data-driven anomaly detection in cyber-physical production systems. Autom. 63(10): 821-832 (2015) - [j1]Jürgen Jasperneite, Sven Hinrichsen, Oliver Niggemann:
"Plug-and-Produce" für Fertigungssysteme - Anwendungsfälle und Lösungsansätze. Inform. Spektrum 38(3): 183-190 (2015) - [c63]Oliver Niggemann, Volker Lohweg:
On the Diagnosis of Cyber-Physical Production Systems. AAAI 2015: 4119-4126 - [c62]Jens Otto, Oliver Niggemann:
Automatic Parameterization of Automation Software for Plug-and-Produce. AAAI Workshop: Algorithm Configuration 2015 - [c61]Peng Li, Jens Eickmeyer, Oliver Niggemann:
Data Driven Condition Monitoring of Wind Power Plants Using Cluster Analysis. CyberC 2015: 131-136 - [c60]Anas Anis, Wilhelm Schäfer, Andrey Pines, Oliver Niggemann:
CP3L: A Cyber-Physical Production Planning Language. ETFA 2015: 1-4 - [c59]Ganesh Man Shrestha, Oliver Niggemann:
Hybrid approach combining Bayesian network and rule-based systems for resource optimization in industrial cleaning processes. ETFA 2015: 1-4 - [c58]Felix Specht, Holger Flatt, Jens Eickmeyer, Oliver Niggemann:
Exploiting multicore processors in PLCs using libraries for IEC 61131-3. ETFA 2015: 1-7 - [c57]Stefan Windmann, Florian Jungbluth, Oliver Niggemann:
A HMM-based fault detection method for piecewise stationary industrial processes. ETFA 2015: 1-6 - [c56]Stefan Windmann, Oliver Niggemann:
MapReduce algorithms for efficient generation of CPS models from large historical data sets. ETFA 2015: 1-4 - [c55]Stefan Windmann, Oliver Niggemann, Heiko Stichweh:
Energy efficiency optimization by automatic coordination of motor speeds in conveying systems. ICIT 2015: 731-737 - [c54]Stefan Windmann, Oliver Niggemann:
Automatic model separation and application for diagnosis in industrial automation systems. ICIT 2015: 1845-1850 - [c53]Ganesh Man Shrestha, Peng Li, Oliver Niggemann:
Bayesian predictive assistance system: An embedded application for resource optimization in industrial cleaning processes. INDIN 2015: 104-109 - [c52]Stefan Windmann, Oliver Niggemann:
Efficient fault detection for industrial automation processes with observable process variables. INDIN 2015: 121-126 - [c51]Stefan Windmann, Jens Eickmeyer, Florian Jungbluth, Johann Badinger, Oliver Niggemann:
Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases. ML4CPS 2015: 45-50 - [c50]Alexander Diedrich, Andreas Bunte, Alexander Maier, Oliver Niggemann:
Kognitive Architektur zum Konzeptlernen in technischen Systemen. ML4CPS 2015: 75-85 - [c49]Jens Eickmeyer, Peng Li, Omid Givehchi, Florian Pethig, Oliver Niggemann:
Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants. DX 2015: 43-50 - [c48]Oliver Niggemann, Gautam Biswas, John S. Kinnebrew, Hamed Khorasgani, Sören Volgmann, Andreas Bunte:
Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control. DX 2015: 185-192 - [c47]Alexander Maier, Oliver Niggemann, Jens Eickmeyer:
On the Learning of Timing Behavior for Anomaly Detection in Cyber-Physical Production Systems. DX 2015: 217-224 - 2014
- [c46]Björn Böttcher, Natalia Moriz, Oliver Niggemann:
From Formal Requirements on Technical Systems to Complete Designs - A Holistic Approach. ECAI 2014: 977-978 - [c45]Sören Volgmann, Francisco M. Rangel Pardo, Oliver Niggemann, Paolo Rosso:
Emotional Trends in Social Media - A State Space Approach. ECAI 2014: 1123-1124 - [c44]Anas Anis, Wilhelm Schäfer, Oliver Niggemann:
A comparison of modeling approaches for planning in Cyber Physical Production Systems. ETFA 2014: 1-8 - [c43]Steffen Henning, Oliver Niggemann, Jens Otto, Sebastian Schriegel:
A descriptive engineering approach for cyber-physical systems. ETFA 2014: 1-4 - [c42]Bjorn Kroll, David Schaffranek, Sebastian Schriegel, Oliver Niggemann:
System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants. ETFA 2014: 1-7 - [c41]Natalia Moriz, Björn Böttcher, Oliver Niggemann, Josef Lackhove:
Assisted design for automation systems - From formal requirements to final designs. ETFA 2014: 1-5 - [c40]Oliver Niggemann, Bjorn Kroll:
On the applicability of model based software development to cyber physical production systems. ETFA 2014: 1-4 - [c39]Ganesh Man Shrestha, Oliver Niggemann:
A Bayesian predictive assistance system for resource optimization - A case study in industrial cleaning process. ETFA 2014: 1-6 - [c38]Sebastian Schriegel, Jürgen Jasperneite, Oliver Niggemann:
Plug and Work für verteilte Echtzeitsysteme mit Zeitsynchronisation. Echtzeit 2014: 11-20 - 2013
- [c37]Björn Böttcher, Johann Badinger, Natalia Moriz, Oliver Niggemann:
Design of industrial automation systems - Formal requirements in the engineering process. ETFA 2013: 1-4 - [c36]Syed Shiraz Gilani, Stefan Windmann, Florian Pethig, Bjorn Kroll, Oliver Niggemann:
The importance of model-learning for the analysis of the energy consumption of production plants. ETFA 2013: 1-8 - [c35]Bjorn Kroll, Sebastian Schriegel, Oliver Niggemann, Stefan Schramm:
A software architecture for the analysis of energy- and process-data. ETFA 2013: 1-4 - [c34]Asmir Vodencarevic, Alexander Maier, Oliver Niggemann:
Evaluating Learning Algorithms for Stochastic Finite Automata - Comparative Empirical Analyses on Learning Models for Technical Systems. ICPRAM 2013: 229-238 - [c33]Stefan Windmann, Shuo Jiao, Oliver Niggemann, Holger Borcherding:
A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems. INDIN 2013: 194-199 - [c32]Jens Otto, Björn Böttcher, Oliver Niggemann:
Plug-and-Produce: Semantic Module Profile. MBEES 2013: 90-99 - 2012
- [c31]Oliver Niggemann, Benno Stein, Asmir Vodencarevic, Alexander Maier, Hans Kleine Büning:
Learning Behavior Models for Hybrid Timed Systems. AAAI 2012 - [c30]Florian Pethig, Bjorn Kroll, Oliver Niggemann, Alexander Maier, Tim Tack, Matthias Maag:
A generic synchronized data acquisition solution for distributed automation systems. ETFA 2012: 1-8 - [c29]