Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2018 (Technologien fr die intelligente Automation Book 9)

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo Front Matter ....Pages I-VII Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project (Christian Beecks, Shreekantha Devasya, Ruben Schlutter)....Pages 1-6 Deduction of time-dependent machine tool characteristics by fuzzy-clustering (Uwe Frieß, Martin Kolouch, Matthias Putz)....Pages 7-17 Unsupervised Anomaly Detection in Production Lines (Alexander Graß, Christian Beecks, Jose Angel Carvajal Soto)....Pages 18-25 A Random Forest Based Classifier for Error Prediction of Highly Individualized Products (Gerd Gröner)....Pages 26-35 Web-based Machine Learning Platform for Condition- Monitoring (Thomas Bernard, Christian Kühnert, Enrique Campbell)....Pages 36-45 Selection and Application of Machine Learning- Algorithms in Production Quality (Jonathan Krauß, Maik Frye, Gustavo Teodoro Döhler Beck, Robert H. Schmitt)....Pages 46-57 Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data? (Oliver Rettig, Silvan Müller, Marcus Strand, Darko Katic)....Pages 58-65 GPU GEMM-Kernel Autotuning for scalable machine learners (Johannes Sailer, Christian Frey, Christian Kühnert)....Pages 66-76 Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria (Anke Stoll, Norbert Pierschel, Ken Wenzel, Tino Langer)....Pages 77-86 A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance (Klaudia Kovacs, Fazel Ansari, Claudio Geisert, Eckart Uhlmann, Robert Glawar, Wilfried Sihn)....Pages 87-96 Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality (Christian Kühnert, Christian Frey, Ruben Seyboldt)....Pages 97-106 Enabling Self-Diagnosis of Automation Devices through Industrial Analytics (Carlos Paiz Gatica, Alexander Boschmann)....Pages 107-115 Making Industrial Analytics work for Factory Automation Applications (Markus Koester)....Pages 116-122 Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems (Andreas Kuhnle, Gisela Lanza)....Pages 123-132 LoRaWan for Smarter Management of Water Network: From metering to data analysis (Jorge Francés-Chust, Joaquín Izquierdo, Idel Montalvo)....Pages 133-136 This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo