Enhancing the control of industrial processes and the quality of products can be supported by learning from sensor data. Project B3 focuses on the investigation of how decentralised data mining can be used for real-time quality predictions and how it can be integrated into production processes.
The tasks of data analytics in production systems have been ordered according to their difficulty: (1) anomaly detection, (2) diagnostic analytics, (3) predictive analytics, and (4) prescriptive analytics. In the first phase, we succeeded in developing a new decentralised anomaly detection and invented learning from label proportions. In the second phase, we investigated diagnostic and predictive analytics. Diagnostic analytics aims at explaining not ok products by process data. Based on time series data from a hot rolling process for steel-bars production, features have been extracted and aggregated that help to distinguish between ok and not ok products. Modeling the overall preprocessing in the software RapidMiner took the toolbox to its extreme, becoming a high-level programming environment. The advantage is the reproducibility of RapidMiner processes and their understandable documentation. Predictive analytics in flexible production processes has contributed to adequate process control in real-time. For distributed data mining, the training of local models from counts (TLMC) for vertically partitioned data has been developed. The anomaly detection has been enhanced for distributed settings as they are given by the Internet of Things. In the third phase, prescriptive analytics will be the focus of the project; i.e., the learned model changes the process in real-time. An example is not only to recognise a deviation of sensor measurements from the normal, but also to adapt the process parameters accordingly.
Saadallah/etal/2022a | Saadallah, Amal and Büscher, Jan and Abdulaaty, Omar and Panusch,Thorben and Deuse,Jochen and Morik, Katharina. Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing. In 55th CIRP conference on Manufacturing Systems, Elsevier, 2022. |
Saadallah/etal/2022c | Saadallah, Amal and Abdulaaty, Omar and Büscher, Jan and Panusch,Thorben and Morik, Katharina and Deuse,Jochen. Early Quality Prediction using Deep Learning on Time Series Sensor Data. In 55th CIRP conference on Manufacturing Systems, Elsevier, 2022. |
Saadallah/etal/2022d | Saadallah, Amal and Finkeldey, Felix and Buß, Jens and Morik, Katharina and Wiederkehr, Petra and Rhode, Wolfgang. Simulation and Sensor Data Fusion for Machine Learning Application. In Advanced Engineering Informatics, Vol. 52, pages 101600, 2022. |
Cao/etal/2021a | Cao, Ba-Tung and Saadallah, Amal and Egorov, Alexey and Freitag, Steffen and Meschke, Günther and Morik, Katharina. Online Geological Anomaly Detection Using Machine Learning in Mechanized Tunneling. In In: Barla M., Di Donna A., Sterpi D. (eds) (editors), Challenges and Innovations in Geomechanics, Vol. vol 125., pages 323--330, Springer, 2021. |
Saadallah/etal/2021a | Saadallah, Amal and Tavakol, Maryam and Katharina, Morik. An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. In The 37th IEEE International Conference on Data Engineering (ICDE), 2021. |
Saadallah/Morik/2021a | Saadallah, Amal and Katharina, Morik. Meta-Adversarial Training of Neural Networks for Binary Classification. In IJCNN International Joint Conference on Neural Networks, 2021. |
Wiederkehr/etal/2021a | Wiederkehr, Petra and Finkeldey, Felix and Merhofe, Torben. Augmented semantic segmentation for the digitization of grinding tools based on deep learning. In CIRP Annals, Vol. 70, No. 1, pages 297--300, Elsevier, 2021. |
Finkeldey/etal/2020a | Finkeldey, Felix and Saadallah, Amal and Wiederkehr, Petra and Morik, Katharina. Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. In Engineering Applications of Artificial Intelligence, Vol. 94, 2020. |
Finkeldey/etal/2020b | Finkeldey, Felix and Wirtz, Andreas and Merhofe, Torben and Wiederkehr, Petra. Learning-Based Prediction of Pose-Dependent Dynamics. In Journal of Manufacturing and Materials Processing, Vol. 4, No. 3, 2020. |
Nanni/etal/2020a | Nanni, Mirco and Gennady, Andrienko and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Commande, Giovanni and Conti, Marco and Cote, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Giannotti, Fosca and Guidotti, Riccardo and Helbng, Dirk and Kaski, Kimmo and Kertesz, Janos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Megias, Jimenez, David Megias and Monreale, Anna and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Pedreschi, Dino and Pentland, Alex and Pianesi, Fabio and Pratesi, Francesca and Rinzivillo, Salvatore and Ruggieri, Salvatore and Siebes, Arno and Torra, Vicenc and Trasarti, Roberto and van der Hoven, Jeroen and Vespignani, Alessandro. Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. In Transactions on Data Privacy, Vol. 13, pages 61--66, 2020. |
Saadallah/Morik/2020g | Saadallah, Amal and Katharina, Morik. Active Sampling for Learning Interpretable Surrogate Machine Learning Models. In IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2020. |
Schmitt/etal/2020a | Schmitt, Jacqueline and Boenig, Jochen and Borggraefe, Thorbjoern and Beitinger, Gunter and Deuse, Jochen. Predictive model-based quality inspection in electronics manufacturing using Machine Learning and Edge Cloud Computing. In Advanced Engineering Informatics (ADVEI), 2020. |
Schulte/etal/2020a | Schulte, Lukas and Schmitt, Jacqueline and Meierhofer, Florian and Deuse, Jochen. Optimizing Inspection Process Severity by Machine Learning Under Label Uncertainty. In Nunes, Isabel L. (editors), Advances in Human Factors and Systems Interaction, pages 3--9, Cham, Springer, 2020. |
Bunse/etal/2019a | Bunse, Mirko and Saadallah, Amal and Morik, Katharina. Towards Active Simulation Data Mining. In Kottke, Daniel and Lemaire, Vincent and Calma, Adrian and Krempl, Georg and Holzinger, Andreas (editors), Proc. of the 3rd Int. Tutorial and Workshop on Interactive Adaptive Learning at ECML-PKDD 2019, Vol. 2444, pages 104--107, CEUR Workshop Proceedings, 2019. |
Deuse/etal/2019a | Deuse, Jochen and Schmitt, Jacqueline and Bönig, Jochen and Beitinger, Gunter. Dynamische Röntgenprüfung in der Elektronikproduktion. Einsatz von Data-Mining-Verfahren zur Qualitätsprognose. In Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), Vol. 114, No. 5, pages 264-267, 2019. |
Deuse/Schmitt/2019a | Deuse, Jochen and Schmitt, Jacqueline. Industrial Data Science - Nutzen Künstlicher Intelligenz für die Produktion. In KANBrief, Vol. 4, 2019. |
Saadallah/Piatkowski/2019a | Saadallah, Amal and Piatkowski, Nico and Finkeldey, Felix and Wiederkehr, Petra and Morik, Katharina. Learning Ensembles in the Presence of Imbalanced Classes. In ICPRAM: 8th international conference on pattern recognition applications and methods - icpram 2019, 2019. |
Saadallah/Priebe/2019c | Saadallah, Amal and Priebe, Florian and Katharina, Morik. A Drift-based Dynamic Ensemble Members Selection using Clustering for Time Series Forecasting. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD 2019, Würzburg, Germany, 2019. |
Schmitt/Deuse/2019a | Schmitt, Jacqueline and Deuse, Jochen. Modellbasierte Prüfprozesse. Einsatz von Data-Mining-Verfahren zur industriellen Qualitätssicherung. In Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), Vol. 114, No. 4, pages 191-193, 2019. |
Schmitt/etal/2019a | Schmitt, Jacqueline and Hahn, Florian and Deuse, Jochen. Practical Framework for Advanced Quality-based Process Control in Interlinked Manufacturing Processes. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pages 511-515, 2019. |
Mertens/etal/2018a | Katharina Mertens and André Barthelmey and René Wöstmann and Jacqueline Schmitt and Christian Harms-Zumbrägel and Tanja Gosch and Jochen Deuse. Retrofit - Von der Brownfield-Anlage zum cyber-physischen System mit dem Ziel der OEE-Verbesserung. In Hubert Biedermann (editors), Predictive Maintenance, pages 173, TÜV Media, 2018. |
Saadallah/etal/2018a | Saadallah, Amal and Finkeldey, Felix and Morik, Katharina and Wiederkehr, Petra. Stability prediction in milling processes using a simulation-based machine learning approach. In 51st CIRP conference on Manufacturing Systems, Elsevier, 2018. |
Schmitt/Deuse/2018a | Schmitt, Jacqueline and Deuse, Jochen. Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pages 700-704, 2018. |
Schmitt/etal/2018a | Schmitt, Jacqueline and Wiegand, Mario and Deuse, Jochen. Qualitätsbasierte Auftragszuordnung - Zuordnung von Zwischenprodukten zu Kundenaufträgen auf Basis von Qualitätsprognosen. In ZWF online, 2018. |
Schmitt/etal/2018b | Schmitt, Jacqueline and Hahn, Florian and Deuse, Jochen. Mathematical modelling of the quality-based order assignment problem. No. 2, Institute of Production Systems, TU Dortmund University, 2018. |
Deuse/etal/2017a | Deuse, J. and Schmitt, J. and Stolpe, M. and Wiegand, M. and Morik, K.. Qualitätsprognosen zur Engpassentlastung in der Injektorfertigung unter Einsatz von Data Mining. In Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V., 2017. |
Krzoska/etal/2017a | Krzoska, Sven and Eickelmann, Michel and Schmitt, Jacqueline and Deuse, Jochen. Data Mining zur Nacharbeitsdauerprognose - Prädiktive Nacharbeitssteuerung und Arbeitsprozessoptimierung für die Montage in der Automobilindustrie. In Werkstatttechnik online, No. 10, pages 773--778, 2017. |
Blom/Morik/2016a | Blom, Hendrik and Morik, Katharina. Resource-Aware Steel Production Through Data Mining. In Berendt, Bettina and Bringmann, Björn and Fromont, Elisa and Garriga, Gemma and Miettinen, Pauli and Tatti, Nikolaj and Tresp, Volker (editors), Machine Learning and Knowledge Discovery in Databases, pages 263--266, Springer, 2016. |
Stolpe/2016a | Marco Stolpe. The Internet of Things: Opportunities and Challenges for Distributed Data Analysis. In SIGKDD Explorations, Vol. 18, No. 1, pages 15-34, 2016. |
Stolpe/etal/2016a | Stolpe, Marco and Blom, Hendrik and Morik, Katharina. Sustainable Industrial Processes by Embedded Real-Time Quality Prediction. In Kersting, Kristian and Lässig, Jörg and Morik, Katharina (editors), Computational Sustainability, pages 201--243, Springer, 2016. |
Stolpe/etal/2016b | Marco Stolpe and Kanishka Bhaduri and Kamalika Das. Distributed Support Vector Machines: An Overview. In Michaelis, S. and Piatkowski, N. and Stolpe, M. (editors), Solving Large Scale Learning Tasks: Challenges and Algorithms, Vol. 9580, pages 109--138, Springer, 2016. |
Wiegand/etal/2016a | Wiegand, Mario and Stolpe, Marco and Deuse, Jochen and Morik, Katharina. Prädiktive Prozessüberwachung auf Basis verteilt erfasster Sensordaten. In at-Automatisierungstechnik, Vol. 64, No. 7, pages 521--533, 2016. |
Eickelmann/etal/2015a | Eickelmann, Michel and Wiegand, Mario and Konrad, Benedikt and Deuse, Jochen. Die Bedeutung von Data Mining im Kontext von Industrie 4.0. In Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), Vol. 110, No. 11, pages 738-743, 2015. |
Liebig/etal/2015a | Liebig, Thomas and Stolpe, Marco and Morik, Katharina. Distributed Traffic Flow Prediction with Label Proportions: From in-Network towards High Performance Computation with MPI. In Proceedings of the 2nd International Workshop on Mining Urban Data (MUD2), Vol. 1392, pages 36--43, CEUR-WS, 2015. |
Stolpe/etal/2015a | Marco Stolpe and Thomas Liebig and Katharina Morik. Communication-efficient learning of traffic flow in a network of wireless presence sensors. In Proceedings of the Workshop on Parallel and Distributed Computing for Knowledge Discovery in Data Bases (PDCKDD 2015), pages (to appear), CEUR-WS, 2015. |
Deuse/etal/2014a | Deuse, Jochen and Wiegand, Mario and Erohin, Olga and Lieber, Daniel and Klinkenberg, Ralf. Big Data Analytics in Produktion und Instandhaltung. In Biedermann, Hubert (editors), Instandhaltung im Wandel. Herausforderungen und Lösungen im Zeitalter von Industrie 4.0, pages 33-48, 2014. |
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Bhaduri/Stolpe/2013a | Bhaduri, Kanishka and Stolpe, Marco. Distributed Data Mining in Sensor Networks. In Aggarwal, Charu C. (editors), Managing and Mining Sensor Data, Berlin, Heidelberg, Springer, 2013. |
Bohnen/etal/2013a | Bohnen, Fabian and Stolpe, Marco and Deuse, Jochen and Morik, Katharina. Using a Clustering Approach with Evolutionary Optimized Attribute Weights to Form Product Families for Production Leveling. In Windt, Katja (editors), Robust Manufacturing Control, pages 189--202, Berlin, Heidelberg, Springer, 2013. |
Bohnen/etal/2013b | Bohnen, Fabian and Buhl, Matthias and Deuse, Jochen. Systematic Procedure for leveling of low volume and high mix production. In CIRP Journal od Manufacturing Science and Technology, Vol. 6, No. 1, pages 53-58, 2013. |
Deuse/etal/2013a | Deuse, Jochen and Konrad, Benedikt and Bohnen, Fabian. Renaissance of Group Technology: Reducing Variability to Match Lean Production Prerequisites. In Bakhtadze, Natalia and Chernyshov, Kirill and Dolgui, Alexandre and Lototsky, Vladimir (editors), Manufacturing Modelling, Management, and Control, Vol. 7, pages 998-1003, 2013. |
Konrad/etal/2013a | Konrad, Benedikt and Lieber, Daniel and Deuse, Jochen. Striving for Zero Defect Production: Intelligent Manufacturing Control through Data Mining in Continuous Rolling Mill Processes. In Windt, Katja (editors), Robust Manufacturing Control, pages 215--229, CIRP, Berlin, Heidelberg, Springer, 2013. |
Lieber/etal/2013a | Lieber, Daniel and Stolpe, Marco and Konrad, Benedikt and Deuse, Jochen and Morik, Katharina. Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning. In Procedia CIRP - 46th CIRP Conf. on Manufacturing Systems, Vol. 7, pages 193-198, Elsevier, 2013. |
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Stolpe/etal/2013a | Stolpe, M. and Bhaduri, K. and Das, K. and Morik, K.. Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines. In Blockeel, Hendrik and Kersting, Kristian and Nijssen, Siegfried and \vZelezný, Filip (editors), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III, pages 321--336, Springer, 2013. |
Lee/etal/2012a | Lee, S. and Stolpe, M. and Morik, K.. Separable Approximate Optimization of Support Vector Machines for Distributed Sensing. In Flach, Peter A. and De Bie, Tijland and Cristianini, Nello (editors), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II, Vol. 7524, pages 387--402, Springer, 2012. |
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Stolpe/etal/2011a | Stolpe, Marco and Morik, Katharina and Konrad, Benedikt and Lieber, Daniel and Deuse, Jochen. Challenges for Data Mining on Sensor Data of Interlinked Processes. In Proceedings of the Next Generation Data Mining Summit (NGDM) 2011, 2011. |
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Stolpe/2017a | Marco Stolpe. Distributed Analysis of Vertically Partitioned Sensor Measurements under Communication Constraints. TU Dortmund University, Dortmund, 2017. |
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