TY - CHAP A1 - Radtke, Maximilian-Peter A1 - Bock, Jürgen ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Combining Knowledge and Deep Learning for Prognostics and Health Management T2 - Proceedings of the European Conference of the PHM Society 2022 N2 - 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. UR - https://doi.org/10.36001/phme.2022.v7i1.3302 KW - Deep Learning KW - Knowledge KW - Hybrid AI Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3302 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27816 SN - 978-1-936263-36-3 SP - 594 EP - 597 PB - PHM Society CY - Rochester ER - TY - JOUR A1 - Hartl, Jakob Joseph Lorenz A1 - Bock, Jürgen T1 - A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes JF - Tehnički glasnik N2 - 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). UR - https://doi.org/10.31803/tg-20240513233046 Y1 - 2024 UR - https://doi.org/10.31803/tg-20240513233046 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49102 SN - 1848-5588 SN - 1846-6168 VL - 18 IS - 3 SP - 437 EP - 444 PB - University North CY - Koprivnica ER - TY - CHAP A1 - Bock, Jürgen A1 - Braun, Daniel A1 - Hülsmann, Jens A1 - Humm, Bernhard A1 - Schmidt, Ute Brigitta A1 - Schumann, Fabienne ED - Beverungen, Daniel ED - Schumann, Jan H. ED - Stich, Volker ED - Strina, Giuseppe T1 - ProDok 4.0 – Intelligente Technische Dokumentation für Industrie 4.0 T2 - Dienstleistungsinnovationen durch Digitalisierung UR - https://doi.org/10.1007/978-3-662-63099-0_10 Y1 - 2021 UR - https://doi.org/10.1007/978-3-662-63099-0_10 SN - 978-3-662-63098-3 SN - 978-3-662-63099-0 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine IS - Band 2: Prozesse – Transformation – Wertschöpfungsnetzwerke SP - 389 EP - 437 PB - Springer Gabler CY - Berlin, Heidelberg ER - TY - CHAP A1 - Beez, Ulrich A1 - Kaupp, Lukas A1 - Deuschel, Tilman A1 - Humm, Bernhard A1 - Schumann, Fabienne A1 - Bock, Jürgen A1 - Hülsmann, Jens ED - Hoppe, Thomas ED - Humm, Bernhard ED - Reibold, Anatol T1 - Context-Aware Documentation in the Smart Factory T2 - Semantic Applications: Methodology, Technology, Corporate Use UR - https://doi.org/10.1007/978-3-662-55433-3_12 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-55433-3_12 SN - 978-3-662-55432-6 SP - 163 EP - 180 PB - Springer Vieweg CY - Berlin ER - TY - CHAP A1 - Malakuti, Somayeh A1 - Bock, Jürgen A1 - Weser, Michael A1 - Venet, Pierre A1 - Zimmermann, Patrick A1 - Wiegand, Mathias A1 - Grothoff, Julian A1 - Wagner, Constantin A1 - Bayha, Andreas T1 - Challenges in Skill-based Engineering of Industrial Automation Systems T2 - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA.2018.8502635 KW - skill KW - property KW - ontology KW - PPR model KW - taxonomy Y1 - 2018 UR - https://doi.org/10.1109/ETFA.2018.8502635 SN - 978-1-5386-7108-5 SN - 1946-0759 SP - 67 EP - 74 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Kaspar, Manuel A1 - Bock, Jürgen A1 - Kogan, Yevgen A1 - Venet, Pierre A1 - Weser, Michael A1 - Zimmermann, Uwe E. T1 - Tool and technology independent function interfaces by using a generic OPC UA representation T2 - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA.2018.8502647 KW - Tools KW - Metadata KW - Robots KW - Servers KW - Semantics KW - Industries KW - Standards Y1 - 2018 UR - https://doi.org/10.1109/ETFA.2018.8502647 SN - 978-1-5386-7108-5 SN - 1946-0759 SP - 1183 EP - 1186 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Diedrich, Christian A1 - Belyaev, Alexander A1 - Schröder, Tizian A1 - Bock, Jürgen A1 - Deppe, Torben A1 - Hankel, Martin A1 - Nehls, Daniel A1 - Marcon, Petr A1 - Pethig, Florian A1 - Reich, Johannes A1 - Vialkowitsch, Jens A1 - Vollmar, Friedrich A1 - Willner, Alexander T1 - Sprache für I4.0-Komponenten – Semantik der Interaktionen von I4.0-Komponenten T2 - AUTOMATION: 19. Leitkongress der Mess- und Automatisierungstechnik / Seamless Convergence of Automation & IT UR - https://doi.org/10.51202/9783181023303-235 KW - Industrie 4.0 KW - I4.0-Komponente KW - Verwaltungsschale KW - Interaktionsmodell Y1 - 2018 UR - https://doi.org/10.51202/9783181023303-235 SN - 978-3-18-092330-7 SN - 0083-5560 SP - 235 EP - 248 PB - VDI Verlag CY - Düsseldorf ER - TY - JOUR A1 - Demiral, Tarik A1 - Bock, Jürgen A1 - Johansson, Pierre T1 - Challenges in Flexible Manufacturing Technologies for the Final Assembly in the Commercial Vehicle Industry JF - Tehnički glasnik N2 - 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. UR - https://doi.org/10.31803/tg-20230417140747 KW - business capabilities KW - final assembly KW - flexibility KW - gap analysis KW - manufacturing Y1 - 2023 UR - https://doi.org/10.31803/tg-20230417140747 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-37880 SN - 1848-5588 VL - 17 IS - 2 SP - 260 EP - 267 PB - University North CY - Koprivnica ER - TY - CHAP A1 - Kaspar, Manuel A1 - Muñoz Osorio, Juan D. A1 - Bock, Jürgen T1 - Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) UR - https://doi.org/10.1109/IROS45743.2020.9341260 KW - Reinforcement learning KW - Aerospace electronics KW - System identification KW - Task analysis KW - Intelligent robots Y1 - 2021 UR - https://doi.org/10.1109/IROS45743.2020.9341260 SN - 978-1-7281-6212-6 SN - 2153-0866 SP - 4383 EP - 4388 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Weser, Michael A1 - Bock, Jürgen A1 - Schmitt, Siwara A1 - Perzylo, Alexander A1 - Evers, Kathrin T1 - An Ontology-based Metamodel for Capability Descriptions T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9212104 KW - Semantic Web KW - Production systems KW - Vocabulary KW - Temperature KW - Taxonomy KW - Semantics KW - Ontologies Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9212104 SN - 978-1-7281-8956-7 SP - 1679 EP - 1686 PB - IEEE CY - Piscataway ER - TY - RPRT A1 - Bayha, Andreas A1 - Bock, Jürgen A1 - Boss, Birgit A1 - Diedrich, Christian A1 - Malakuti, Somayeh T1 - Describing Capabilities of Industrie 4.0 Components BT - Joint White Paper between Plattform Industrie 4.0, VDI GMA 7.20, BaSys 4.2 Y1 - 2020 UR - https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Capabilities_Industrie40_Components.html PB - Bundesministerium für Wirtschaft und Energie CY - Berlin ER - TY - JOUR A1 - Humm, Bernhard A1 - Bense, Hermann A1 - Bock, Jürgen A1 - Classen, Mario A1 - Halvani, Oren A1 - Herta, Christian A1 - Hoppe, Thomas A1 - Juwig, Oliver A1 - Siegel, Melanie T1 - Applying machine intelligence in practice BT - Selected results of the 2019 DagstuhlWorkshop on Applied Machine Intelligence JF - Informatik Spektrum N2 - 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 für 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. UR - https://doi.org/10.1007/s00287-020-01259-2 Y1 - 2020 UR - https://doi.org/10.1007/s00287-020-01259-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-16202 SN - 1432-122X VL - 43 IS - 2 SP - 137 EP - 144 PB - Springer CY - Berlin ER - TY - CHAP A1 - Venet, Pierre A1 - Safronov, Kirill A1 - Ehambram, Aaronkumar A1 - Wagner, Sven A1 - Bock, Jürgen A1 - Zimmermann, Uwe E. T1 - Application of Ontologies for Semantic Scene Segmentation and Object Recognition T2 - ISR 2020: 52th International Symposium on Robotics in conjunction with: automatica Y1 - 2020 UR - https://www.vde-verlag.de/proceedings-de/455428037.html SN - 978-380075429-8 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/document/9307480 SP - 258 EP - 264 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Xian, Zhicong A1 - Chaudhary, Tabish A1 - Bock, Jürgen T1 - Making Sense of Touch BT - unsupervised Shapelet Learning in Bag-of-words Sense T2 - Workshop on New advances in Brain-inspired Perception, Interaction and Learning, International Conference on Robotics and Automation 2020 Y1 - 2020 UR - https://brain-pil.github.io/icra2020/contributions/Brain-PIL_2020_contrib_7.pdf UR - https://github.com/brain-pil/icra2020/blob/f3e5a9b3d265d64d257541000acfa8cf198a178b/contributions/Brain-PIL_2020_contrib_7.pdf ER - TY - INPR A1 - Köcher, Aljosha A1 - Belyaev, Alexander A1 - Hermann, Jesko A1 - Bock, Jürgen A1 - Meixner, Kristof A1 - Volkmann, Magnus A1 - Winter, Michael A1 - Zimmermann, Patrick A1 - Grimm, Stephan A1 - Diedrich, Christian T1 - A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing UR - https://doi.org/10.48550/arXiv.2209.09632 KW - capabilities KW - skill KW - services KW - resource functions KW - standardization Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2209.09632 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Winter, Michael A1 - Bock, Jürgen A1 - Hoos, Johannes A1 - Klausmann, Tobias A1 - Schnebel, Boris A1 - Schmitt, Siwara A1 - Simon, Marco A1 - Stolze, Melanie A1 - Vieira da Silva, Luis Miguel A1 - Kleinert, Tobias ED - Jumar, Ulrich ED - Diedrich, Christian T1 - Methods to Describe, Assign and Derive Capabilities from the Capability, Skill and Service (CSS) Model T2 - 18. Fachtagung, EKA 2024, Entwurf komplexer Automatisierungssysteme, Beschreibungsmittel, Methoden, Werkzeuge und Anwendungen N2 - 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. UR - http://dx.doi.org/10.25673/116039 KW - Industry 4.0 KW - Capability description KW - Resource function KW - Product description KW - Automation engineering Y1 - 2024 UR - http://dx.doi.org/10.25673/116039 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48431 PB - Institut für Automation und Kommunikation CY - Magdeburg ER - TY - CHAP A1 - Bock, Jürgen A1 - Klausmann, Tobias A1 - Kleinert, Tobias A1 - Köcher, Aljosha A1 - Simon, Marco A1 - Winter, Michael ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - A Formal Approach to Defining Effects of Manufacturing Functions T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/etfa61755.2024.10710847 Y1 - 2024 UR - https://doi.org/10.1109/etfa61755.2024.10710847 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Steffel, Pauline A1 - Bock, Jürgen A1 - Schiendorfer, Alexander ED - Almeida, Luis ED - Indria, Marina ED - de Sousa, Mario ED - Visioli, Antonio ED - Ashjaei, Mohammad ED - Santos, Pedro T1 - Towards a Configurable and Reusable RL Training Infrastructure for AMRs in ROS2 T2 - 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA65518.2025.11205702 Y1 - 2025 UR - https://doi.org/10.1109/ETFA65518.2025.11205702 SN - 979-8-3315-5383-8 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Piendl, Daniel A1 - Radtke, Maximilian-Peter A1 - Jacobsen, Hendrik A1 - Bock, Jürgen A1 - Zaeh, Michael T1 - Predictive Maintenance of Ball Screws: A Comparative Study Using Real-World Industrial Data JF - Procedia CIRP N2 - 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. UR - https://doi.org/10.1016/j.procir.2025.02.135 Y1 - 2025 UR - https://doi.org/10.1016/j.procir.2025.02.135 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61629 SN - 2212-8271 VL - 2025 IS - 134 SP - 390 EP - 395 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Köcher, Aljosha A1 - Belyaev, Alexander A1 - Hermann, Jesko A1 - Bock, Jürgen A1 - Meixner, Kristof A1 - Volkmann, Magnus A1 - Winter, Michael A1 - Zimmermann, Patrick A1 - Grimm, Stephan A1 - Diedrich, Christian T1 - A reference model for common understanding of capabilities and skills in manufacturing JF - at - Automatisierungstechnik N2 - 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. N2 - Für viele Anwendungsfälle von Industrie 4.0 werden herstellerneutrale und maschinenlesbare Informationsmodelle von Capabilities und Skills benötigt. In der Praxis sind standardisierte Modelle erforderlich, welche zur Zeit jedoch nicht existieren. In diesem Beitrag wird ein Referenzmodell vorgestellt, das von Mitgliedern verschiedener Organisationen in einer Arbeitsgruppe der Plattform Industrie 4.0 erarbeitet wurde. Dieses Modell umfasst Definitionen der wichtigsten Aspekte von Capabilities und Skills und kann somit als wichtige Grundlage für weitere Standardisierungsarbeiten angesehen werden. T2 - Ein Referenzmodell für ein gemeinsames Verständnis von Capabilities und Skills von Anlagen UR - https://doi.org/10.1515/auto-2022-0117 KW - capabilities KW - resource functions KW - services KW - skill KW - standardization KW - Capabilities KW - Maschinenfunktionen KW - Services KW - Skills KW - Standardisierung Y1 - 2023 UR - https://doi.org/10.1515/auto-2022-0117 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30866 SN - 2196-677X SN - 0178-2312 VL - 71 IS - 2 SP - 94 EP - 104 PB - De Gruyter CY - Berlin ER - TY - INPR A1 - Suciu, Tobias A1 - Bock, Jürgen T1 - A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing T2 - Research Square N2 - 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. UR - https://doi.org/10.21203/rs.3.rs-7778102/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7778102/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64551 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - CHAP A1 - Radtke, Maximilian-Peter A1 - Huber, Marco F. A1 - Bock, Jürgen ED - Kulkarni, Chetan S. ED - Roychoudhury, Indranil T1 - Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs T2 - Proceedings of the Annual Conferenceof the PHM Society 2023 N2 - 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. UR - https://doi.org/10.36001/phmconf.2023.v15i1.3552 Y1 - 2023 UR - https://doi.org/10.36001/phmconf.2023.v15i1.3552 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-43307 PB - PHM Society CY - Rochester ER - TY - CHAP A1 - Radtke, Maximilian-Peter A1 - Huber, Marco F. A1 - Bock, Jürgen ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/etfa61755.2024.10711048 Y1 - 2024 UR - https://doi.org/10.1109/etfa61755.2024.10711048 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Radtke, Maximilian-Peter A1 - Bock, Jürgen ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Expert Knowledge Induced Logic Tensor Networks BT - A Bearing Fault Diagnosis Case Study T2 - Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 N2 - 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. UR - https://doi.org/10.36001/phme.2022.v7i1.3329 KW - Logic Tensor Networks KW - Hybrid AI KW - Expert Knowledge KW - Fault Diagnosis KW - Deep Learning KW - Bearing Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3329 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27803 SN - 978-1-936263-36-3 IS - 1 SP - 421 EP - 431 PB - PHM Society CY - State College ER - TY - RPRT A1 - Diedrich, Christian A1 - Belyaev, Alexander A1 - Blumenfeld, Rafi A1 - Bock, Jürgen A1 - Grimm, Stephan A1 - Hermann, Jesko A1 - Klausmann, Tobias A1 - Köcher, Aljosha A1 - Maurmaier, Matthias A1 - Meixner, Kristof A1 - Peschke, Jörn A1 - Schleipen, Miriam A1 - Schmitt, Siwara A1 - Schnebel, Boris A1 - Stephan, Guido A1 - Volkmann, Magnus A1 - Wannagat, Andreas A1 - Watson, Kym A1 - Winter, Michael A1 - Zimmermann, Patrick T1 - Information Model for Capabilities, Skills & Services: Definition of terminology and proposal for a technology-independent information model for capabilities and skills in flexible manufacturing Y1 - 2022 UR - https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/CapabilitiesSkillsServices.html PB - Plattform Industrie 4.0 CY - Berlin ER -