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 - 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 - 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 - 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 - 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 -