@inproceedings{RadtkeBock2022, author = {Radtke, Maximilian-Peter and Bock, J{\"u}rgen}, title = {Combining Knowledge and Deep Learning for Prognostics and Health Management}, booktitle = {Proceedings of the European Conference of the PHM Society 2022}, editor = {Do, Phuc and Michau, Gabriel and Ezhilarasu, Cordelia}, publisher = {PHM Society}, address = {Rochester}, isbn = {978-1-936263-36-3}, doi = {https://doi.org/10.36001/phme.2022.v7i1.3302}, pages = {594 -- 597}, year = {2022}, abstract = {In the recent past deep learning approaches have achieved remarkable results in the area of Prognostics and Health Management (PHM). These algorithms rely on large amounts of data, which is often not available, and produce outputs, which are hard to interpret. Before the broad success of deep learning machine faults were often classified using domain expert knowledge based on experience and physical models. In comparison, these approaches only require small amounts of data and produce highly interpretable results. On the downside, however, they struggle to predict unexpected patterns hidden in data. This research aims to combine knowledge and deep learning to increase accuracy, robustness and interpretability of current models.}, language = {en} } @article{HartlBock2024, author = {Hartl, Jakob Joseph Lorenz and Bock, J{\"u}rgen}, title = {A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes}, volume = {18}, journal = {Tehnički glasnik}, number = {3}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20240513233046}, pages = {437 -- 444}, year = {2024}, abstract = {The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).}, language = {en} } @inbook{BockBraunHuelsmannetal.2021, author = {Bock, J{\"u}rgen and Braun, Daniel and H{\"u}lsmann, Jens and Humm, Bernhard and Schmidt, Ute Brigitta and Schumann, Fabienne}, title = {ProDok 4.0 - Intelligente Technische Dokumentation f{\"u}r Industrie 4.0}, booktitle = {Dienstleistungsinnovationen durch Digitalisierung}, number = {Band 2: Prozesse - Transformation - Wertsch{\"o}pfungsnetzwerke}, editor = {Beverungen, Daniel and Schumann, Jan H. and Stich, Volker and Strina, Giuseppe}, publisher = {Springer Gabler}, address = {Berlin, Heidelberg}, isbn = {978-3-662-63098-3}, doi = {https://doi.org/10.1007/978-3-662-63099-0_10}, pages = {389 -- 437}, year = {2021}, language = {de} } @inbook{BeezKauppDeuscheletal.2018, author = {Beez, Ulrich and Kaupp, Lukas and Deuschel, Tilman and Humm, Bernhard and Schumann, Fabienne and Bock, J{\"u}rgen and H{\"u}lsmann, Jens}, title = {Context-Aware Documentation in the Smart Factory}, booktitle = {Semantic Applications: Methodology, Technology, Corporate Use}, editor = {Hoppe, Thomas and Humm, Bernhard and Reibold, Anatol}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-55432-6}, doi = {https://doi.org/10.1007/978-3-662-55433-3_12}, pages = {163 -- 180}, year = {2018}, language = {en} } @inproceedings{MalakutiBockWeseretal.2018, author = {Malakuti, Somayeh and Bock, J{\"u}rgen and Weser, Michael and Venet, Pierre and Zimmermann, Patrick and Wiegand, Mathias and Grothoff, Julian and Wagner, Constantin and Bayha, Andreas}, title = {Challenges in Skill-based Engineering of Industrial Automation Systems}, booktitle = {2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-5386-7108-5}, issn = {1946-0759}, doi = {https://doi.org/10.1109/ETFA.2018.8502635}, pages = {67 -- 74}, year = {2018}, language = {en} } @inproceedings{KasparBockKoganetal.2018, author = {Kaspar, Manuel and Bock, J{\"u}rgen and Kogan, Yevgen and Venet, Pierre and Weser, Michael and Zimmermann, Uwe E.}, title = {Tool and technology independent function interfaces by using a generic OPC UA representation}, booktitle = {2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-5386-7108-5}, issn = {1946-0759}, doi = {https://doi.org/10.1109/ETFA.2018.8502647}, pages = {1183 -- 1186}, year = {2018}, language = {en} } @inproceedings{DiedrichBelyaevSchroederetal.2018, author = {Diedrich, Christian and Belyaev, Alexander and Schr{\"o}der, Tizian and Bock, J{\"u}rgen and Deppe, Torben and Hankel, Martin and Nehls, Daniel and Marcon, Petr and Pethig, Florian and Reich, Johannes and Vialkowitsch, Jens and Vollmar, Friedrich and Willner, Alexander}, title = {Sprache f{\"u}r I4.0-Komponenten - Semantik der Interaktionen von I4.0-Komponenten}, booktitle = {AUTOMATION: 19. Leitkongress der Mess- und Automatisierungstechnik / Seamless Convergence of Automation \& IT}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-092330-7}, issn = {0083-5560}, doi = {https://doi.org/10.51202/9783181023303-235}, pages = {235 -- 248}, year = {2018}, language = {en} } @article{DemiralBockJohansson2023, author = {Demiral, Tarik and Bock, J{\"u}rgen and Johansson, Pierre}, title = {Challenges in Flexible Manufacturing Technologies for the Final Assembly in the Commercial Vehicle Industry}, volume = {17}, journal = {Tehnički glasnik}, number = {2}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20230417140747}, pages = {260 -- 267}, year = {2023}, abstract = {Increasing customer demands and product diversity as well as emerging technologies and market trends, such as the establishment of new driveline technologies, like e-mobility or hydrogen, present challenges for manufacturing companies in the commercial vehicle industry. Consequently, companies must strengthen their focus to the aspect of flexibility within their manufacturing processes. This paper contributes to the state of the art in flexible manufacturing technologies research, which enables manufacturing companies to deal with these increasing flexibility requirements. Focussing on the area of final assembly the paper takes a holistic perspective and characterizes the readiness of automotive companies to be able to implement flexible manufacturing technologies. The system ecosystem and the process organization of automotive companies are examined with respect to the requirements of flexible manufacturing. Finally, gaps that hinder the implementation of flexible manufacturing technologies are identified and described, and possible solution concepts for the identified gaps are proposed.}, language = {en} } @inproceedings{KasparMunozOsorioBock2021, author = {Kaspar, Manuel and Mu{\~n}oz Osorio, Juan D. and Bock, J{\"u}rgen}, title = {Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-7281-6212-6}, issn = {2153-0866}, doi = {https://doi.org/10.1109/IROS45743.2020.9341260}, pages = {4383 -- 4388}, year = {2021}, language = {en} } @inproceedings{WeserBockSchmittetal.2020, author = {Weser, Michael and Bock, J{\"u}rgen and Schmitt, Siwara and Perzylo, Alexander and Evers, Kathrin}, title = {An Ontology-based Metamodel for Capability Descriptions}, booktitle = {Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-8956-7}, doi = {https://doi.org/10.1109/ETFA46521.2020.9212104}, pages = {1679 -- 1686}, year = {2020}, language = {en} } @techreport{BayhaBockBossetal.2020, author = {Bayha, Andreas and Bock, J{\"u}rgen and Boss, Birgit and Diedrich, Christian and Malakuti, Somayeh}, title = {Describing Capabilities of Industrie 4.0 Components}, subtitle = {Joint White Paper between Plattform Industrie 4.0, VDI GMA 7.20, BaSys 4.2}, institution = {Gesch{\"a}ftsstelle Plattform Industrie 4.0}, publisher = {Bundesministerium f{\"u}r Wirtschaft und Energie}, address = {Berlin}, url = {https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Capabilities_Industrie40_Components.html}, pages = {33}, year = {2020}, language = {en} } @article{HummBenseBocketal.2020, author = {Humm, Bernhard and Bense, Hermann and Bock, J{\"u}rgen and Classen, Mario and Halvani, Oren and Herta, Christian and Hoppe, Thomas and Juwig, Oliver and Siegel, Melanie}, title = {Applying machine intelligence in practice}, volume = {43}, journal = {Informatik Spektrum}, subtitle = {Selected results of the 2019 DagstuhlWorkshop on Applied Machine Intelligence}, number = {2}, publisher = {Springer}, address = {Berlin}, issn = {1432-122X}, doi = {https://doi.org/10.1007/s00287-020-01259-2}, pages = {137 -- 144}, year = {2020}, abstract = {The relevance of Machine Intelligence, a.k.a. Artificial Intelligence (AI), is undisputed at the present time. This is not only due to AI successes in research but, more prominently, its use in day-to-day practice. In 2014, we started a series of annual workshops at the Leibniz Zentrum f{\"u}r Informatik, Schloss Dagstuhl, Germany, initially focussing on Corporate Semantic Web and later widening the scope to Applied Machine Intelligence. This article presents a number of AI applications from various application domains, including medicine, industrial manufacturing and the insurance sector. Best practices, current trends, possibilities and limitations of new AI approaches for developing AI applications are also presented. Focus is put on the areas of natural language processing, ontologies and machine learning. The article concludes with a summary and outlook.}, language = {en} } @inproceedings{VenetSafronovEhambrametal.2020, author = {Venet, Pierre and Safronov, Kirill and Ehambram, Aaronkumar and Wagner, Sven and Bock, J{\"u}rgen and Zimmermann, Uwe E.}, title = {Application of Ontologies for Semantic Scene Segmentation and Object Recognition}, booktitle = {ISR 2020: 52th International Symposium on Robotics in conjunction with: automatica}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-380075429-8}, url = {https://www.vde-verlag.de/proceedings-de/455428037.html}, pages = {258 -- 264}, year = {2020}, language = {en} } @inproceedings{XianChaudharyBock2020, author = {Xian, Zhicong and Chaudhary, Tabish and Bock, J{\"u}rgen}, title = {Making Sense of Touch}, booktitle = {Workshop on New advances in Brain-inspired Perception, Interaction and Learning, International Conference on Robotics and Automation 2020}, subtitle = {unsupervised Shapelet Learning in Bag-of-words Sense}, url = {https://brain-pil.github.io/icra2020/contributions/Brain-PIL_2020_contrib_7.pdf}, year = {2020}, language = {en} } @unpublished{KoecherBelyaevHermannetal.2022, author = {K{\"o}cher, Aljosha and Belyaev, Alexander and Hermann, Jesko and Bock, J{\"u}rgen and Meixner, Kristof and Volkmann, Magnus and Winter, Michael and Zimmermann, Patrick and Grimm, Stephan and Diedrich, Christian}, title = {A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2209.09632}, year = {2022}, language = {en} } @inproceedings{WinterBockHoosetal.2024, author = {Winter, Michael and Bock, J{\"u}rgen and Hoos, Johannes and Klausmann, Tobias and Schnebel, Boris and Schmitt, Siwara and Simon, Marco and Stolze, Melanie and Vieira da Silva, Luis Miguel and Kleinert, Tobias}, title = {Methods to Describe, Assign and Derive Capabilities from the Capability, Skill and Service (CSS) Model}, booktitle = {18. Fachtagung, EKA 2024, Entwurf komplexer Automatisierungssysteme, Beschreibungsmittel, Methoden, Werkzeuge und Anwendungen}, editor = {Jumar, Ulrich and Diedrich, Christian}, publisher = {Institut f{\"u}r Automation und Kommunikation}, address = {Magdeburg}, doi = {http://dx.doi.org/10.25673/116039}, year = {2024}, abstract = {For the use of the CSS model, workflows are needed to describe, assign and derive capabilities. This contribution provides a Capability Determining method that encompasses the three underlying technology-independent methods to guide stakeholders in describing, assigning and deriving capabilities for manufacturing. The first underlying method, Describing Capabilities, describes a workflow for the formal semantic description of capabilities. The workflow for assigning capabilities to a production resource is explained using the second method Assigning Capability. In the last workflow, capabilities are derived from production specification or a process description using the method Deriving Capability.}, language = {en} } @inproceedings{BockKlausmannKleinertetal.2024, author = {Bock, J{\"u}rgen and Klausmann, Tobias and Kleinert, Tobias and K{\"o}cher, Aljosha and Simon, Marco and Winter, Michael}, title = {A Formal Approach to Defining Effects of Manufacturing Functions}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/etfa61755.2024.10710847}, year = {2024}, language = {en} } @inproceedings{SteffelBockSchiendorfer2025, author = {Steffel, Pauline and Bock, J{\"u}rgen and Schiendorfer, Alexander}, title = {Towards a Configurable and Reusable RL Training Infrastructure for AMRs in ROS2}, booktitle = {2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Almeida, Luis and Indria, Marina and de Sousa, Mario and Visioli, Antonio and Ashjaei, Mohammad and Santos, Pedro}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-5383-8}, doi = {https://doi.org/10.1109/ETFA65518.2025.11205702}, year = {2025}, language = {en} } @article{PiendlRadtkeJacobsenetal.2025, author = {Piendl, Daniel and Radtke, Maximilian-Peter and Jacobsen, Hendrik and Bock, J{\"u}rgen and Zaeh, Michael}, title = {Predictive Maintenance of Ball Screws: A Comparative Study Using Real-World Industrial Data}, volume = {2025}, journal = {Procedia CIRP}, number = {134}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {https://doi.org/10.1016/j.procir.2025.02.135}, pages = {390 -- 395}, year = {2025}, abstract = {Ball screws are widely used in machine tool feed drives. With increasing degradation of the ball screws, machining accuracy and economic efficiency decrease. Past investigations have shown that condition monitoring models can predict this degradation. However, these models are typically trained and evaluated using datasets derived from test benches, questioning their applicability to real machine tools. In this article, a comparative evaluation of a selection of these condition monitoring models using an industrial dataset is described. This dataset consists of measurement data from a total of nine ball screws used in three machine tools until failure. It was shown that when using data of multiple ball screws or machines, artificial neural networks or automated machine learning methods achieve a higher accuracy than statistical methods. However, for smaller datasets, statistical methods perform almost as well. The results provide an insight into the industrial applicability of the evaluated condition monitoring models.}, language = {en} } @article{KoecherBelyaevHermannetal.2023, author = {K{\"o}cher, Aljosha and Belyaev, Alexander and Hermann, Jesko and Bock, J{\"u}rgen and Meixner, Kristof and Volkmann, Magnus and Winter, Michael and Zimmermann, Patrick and Grimm, Stephan and Diedrich, Christian}, title = {A reference model for common understanding of capabilities and skills in manufacturing}, volume = {71}, journal = {at - Automatisierungstechnik}, number = {2}, publisher = {De Gruyter}, address = {Berlin}, issn = {2196-677X}, doi = {https://doi.org/10.1515/auto-2022-0117}, pages = {94 -- 104}, year = {2023}, abstract = {In manufacturing, many use cases of Industrie 4.0 require vendor-neutral and machine-interpretable information models to describe, implement and execute resource functions. Such models have been researched under the terms capabilities and skills. Standardization of such models is required, but currently not available. This paper presents a reference model developed jointly by members of various organizations in a working group of the Plattform Industrie 4.0. This model covers definitions of most important aspects of capabilities and skills. It can be seen as a basis for further standardization efforts.}, language = {en} } @unpublished{SuciuBock2025, author = {Suciu, Tobias and Bock, J{\"u}rgen}, title = {A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-7778102/v1}, year = {2025}, abstract = {The increasing complexity of modern vehicles and their testing procedures generates vast amounts of multivariate time series data, making manual anomaly detection during automotive testing increasingly challenging. This article investigates the application of deep learning algorithms for automated anomaly detection in automotive bus data collected during dynamic driving scenarios. Three distinct architectures are implemented and compared: a CNN-based forecasting approach (DeepAnT), an LSTM-based model (LSTM-AD), and a Convolutional Autoencoder (CAE). Real-world driving data collected across various scenarios, ranging from normal operation to extreme maneuvers, is employed. Through evaluation across seven distinct test scenarios, findings reveal that while each architecture demonstrates specific strengths, their effectiveness varies significantly based on anomaly type and driving context. DeepAnT shows the most consistent performance across different scenarios, while LSTM-AD achieves superior detection capability for complex temporal patterns, particularly in scenarios involving coordinated changes across multiple features. The CAE excels at identifying pronounced deviations but shows limitations in detecting subtle anomalies. This study demonstrates that while deep learning models effectively detect anomalies in automotive time series data, their practical implementation requires careful consideration of specific use cases, emphasizing the critical role of data preprocessing and threshold calculation in ensuring reliable anomaly detection.}, language = {en} } @inproceedings{RadtkeHuberBock2023, author = {Radtke, Maximilian-Peter and Huber, Marco F. and Bock, J{\"u}rgen}, title = {Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs}, booktitle = {Proceedings of the Annual Conferenceof the PHM Society 2023}, editor = {Kulkarni, Chetan S. and Roychoudhury, Indranil}, publisher = {PHM Society}, address = {Rochester}, doi = {https://doi.org/10.36001/phmconf.2023.v15i1.3552}, year = {2023}, abstract = {In the realm of prognostics and health management (PHM), it is common to possess not only process data but also domain knowledge, which, if integrated into data-driven algorithms, can aid in solving specific tasks. This paper explores the integration of knowledge graphs (KGs) into deep learning models to develop a more resilient approach capable of handling domain shifts, such as variations in machine operating conditions. We present and assess a KG-enhanced deep learning approach in a representative PHM use case, demonstrating its effectiveness by incorporating domain-invariant knowledge through the KG. Furthermore, we provide guidance for constructing a comprehensive hierarchical KG representation that preserves semantic information while facilitating numerical representation. The experimental results showcase the improved performance and domain shift robustness of the KG-enhanced approach in fault diagnostics.}, language = {en} } @inproceedings{RadtkeHuberBock2024, author = {Radtke, Maximilian-Peter and Huber, Marco F. and Bock, J{\"u}rgen}, title = {Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/etfa61755.2024.10711048}, year = {2024}, language = {en} } @inproceedings{RadtkeBock2022, author = {Radtke, Maximilian-Peter and Bock, J{\"u}rgen}, title = {Expert Knowledge Induced Logic Tensor Networks}, booktitle = {Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022}, subtitle = {A Bearing Fault Diagnosis Case Study}, number = {1}, editor = {Do, Phuc and Michau, Gabriel and Ezhilarasu, Cordelia}, publisher = {PHM Society}, address = {State College}, isbn = {978-1-936263-36-3}, doi = {https://doi.org/10.36001/phme.2022.v7i1.3329}, pages = {421 -- 431}, year = {2022}, abstract = {In the recent past deep learning approaches have achieved some remarkable results in the area of fault diagnostics and anomaly detection. Nevertheless, these algorithms rely on large amounts of data, which is often not available, and produce outputs, which are hard to interpret. These deficiencies make real life applications difficult. Before the broad success of deep learning machine faults were often classified using domain expert knowledge based on experience and physical models. In comparison, these approaches only require small amounts of data and produce highly interpretable results. On the downside, however, they struggle to predict unexpected patterns hidden in data. Merging these two concepts promises to increase accuracy, robustness and interpretability of models. In this paper we present a hybrid approach to combine expert knowledge with deep learning and evaluate it on rolling element bearing fault detection. First, we create a knowledge base for fault classification derived from the expected physical attributes of different faults in the envelope spectrum of vibration signals. This knowledge is used to derive a similarity function for comparing input signals to expected faulty signals. Afterwards, the similarity measure is incorporated into different neural networks using a Logic Tensor Network (LTN). This enables logical reasoning in the loss function, in which we aim to mimic the decision process of an expert analyzing the input data. Further, we extend LTNs by weight schedules for axiom groups. We show that our approach outperforms the baseline models on two bearing fault data sets with different attributes and directly gives a better understanding of whether or not fault signals are influenced by other effects or behave as expected.}, language = {en} } @techreport{DiedrichBelyaevBlumenfeldetal.2022, author = {Diedrich, Christian and Belyaev, Alexander and Blumenfeld, Rafi and Bock, J{\"u}rgen and Grimm, Stephan and Hermann, Jesko and Klausmann, Tobias and K{\"o}cher, Aljosha and Maurmaier, Matthias and Meixner, Kristof and Peschke, J{\"o}rn and Schleipen, Miriam and Schmitt, Siwara and Schnebel, Boris and Stephan, Guido and Volkmann, Magnus and Wannagat, Andreas and Watson, Kym and Winter, Michael and Zimmermann, Patrick}, title = {Information Model for Capabilities, Skills \& Services: Definition of terminology and proposal for a technology-independent information model for capabilities and skills in flexible manufacturing}, publisher = {Plattform Industrie 4.0}, address = {Berlin}, url = {https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/CapabilitiesSkillsServices.html}, pages = {46}, year = {2022}, language = {en} }