Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2017 (Technologien für die intelligente Automation, 11)

The work 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 Lemgo, October 25th-26th, 2017. 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. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. 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 The work 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 Lemgo, October 25th-26th, 2017. 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. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. 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 Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks (Fazel Ansari, Robert Glawar, Wilfried Sihn)....Pages 1-8 Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors (Martin Lachmann, Tilman Stark, Martin Golz, Eberhard Manske)....Pages 9-16 Differential Evolution in Production Process Optimization of Cyber Physical Systems (Katharina Giese, Jens Eickmeyer, Oliver Niggemann)....Pages 17-23 Machine Learning for Process-X: A Taxonomy (Felix Reinhart, Sebastian von Enzberg, Arno Kühn, Roman Dumitrescu)....Pages 25-33 Intelligent edge processing (Ljiljana Stojanovic)....Pages 35-42 Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. (Andreas Bunte, Peng Li, Oliver Niggemann)....Pages 43-51 Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis (Marta Fullen, Peter Schüller, Oliver Niggemann)....Pages 53-61 Verstehen von Maschinenverhalten mit Hilfe von Machine Learning (Heinrich Warkentin, Meike Wocken, Alexander Maier)....Pages 63-71 Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures (Carlos Paiz Gatica, Marco Platzner)....Pages 73-80 The Acoustic Test System for Transmissions in the VW Group (Thomas Lewien, Ivan Slimak, Pyare Püschel)....Pages 81-87