Table Of ContentTechnologien für die intelligente Automation
Technologies for Intelligent Automation
Jürgen Beyerer
Christian Kühnert
Oliver Niggemann Editors
Machine Learning
for Cyber Physical
Systems
Selected papers from the International
Conference ML4CPS 2018
Technologien für die intelligente Automation
Technologies for Intelligent Automation
Band 9
Reihe herausgegeben von
inIT - Institut für industrielle Informa
Lemgo, Deutschland
Z iel der Buchreihe ist die Publikation neuer Ansätze in der Automation auf wissenschaftli-
chem Niveau, Themen, die heute und in Zukunft entscheidend sind, für die deutsche und
internationale Industrie und Forschung. Initiativen wie Industrie 4.0, Industrial Internet
oder Cyber-physical Systems machen dies deutlich. Die Anwendbarkeit und der indus-
trielle Nutzen als durchgehendes Leitmotiv der Veröffentlichungen stehen dabei im
Vordergrund. Durch diese Verankerung in der Praxis wird sowohl die Verständlichkeit als
auch die Relevanz der Beiträge für die Industrie und für die angewandte Forschung gesi-
chert. Diese Buchreihe möchte Lesern eine Orientierung für die neuen Technologien und
deren Anwendungen geben und so zur erfolgreichen Umsetzung der Initiativen beitragen.
Weitere Bände in der Reihe h ttp://www.springer.com/series/13886
Jürgen Beyerer · Christian Kühnert
Oliver Niggemann
Editors
Machine Learning for Cyber
Physical Systems
Selected papers from the International
Conference ML4CPS 2018
Editors
Jürgen Beyerer Christian Kühnert
Institut für Optronik, Systemtechnik und MRD
Bildauswertung Fraunhofer Institute for Optronics,
Fraunhofer System Technologies and Image Exploitation
Karlsruhe, Germany IOSB
Karlsruhe, Germany
Oliver Niggemann
inIT - Institut für industrielle
Informationstechnik
Hochschule Ostwestfalen-Lippe
Lemgo, Germany
ISSN 2522-8579 ISSN 2522-8587 (electronic)
Technologien für die intelligente Automation
ISBN 978-3-662-58484-2 ISBN 978-3-662-58485-9 (eBook)
h ttps://doi.org/10.1007/978-3-662-58485-9
Library of Congress Control Number: 2018965223
Springer Vieweg
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Preface
Cyber Physical Systems are characterized by their ability to adapt and to learn.
They analyze their environment, learn patterns, and they are able to generate
predictions. Typical applications are condition monitoring, predictive mainte
nance, image processing and diagnosis. Machine Learning is the key technology
for these developments.
The fourth conference on Machine Learning for Cyber-Physical-Systems and
Industry 4.0 - ML4CPS - was held at the Fraunhofer IOSB in Karlsruhe, on
October 23.rd and 24.th 2018. The aim of the conference is to provide a forum
to present new approaches, discuss experiences and to develop visions in the
area of data analysis for cyber-physical systems. This book provides the pro
ceedings of selected contributions presented at the ML4CPS 2018.
The editors would like to thank all contributors that led to a pleasant and
rewarding conference. Additionally, the editors would like to thank all reviewers
for sharing their time and expertise with the authors. It is hoped that these
proceedings will form a valuable addition to the scientific and developmental
knowledge in the research fields of machine learning, information fusion, system
technologies and industry 4.0.
Prof. Dr.-Ing. Jiirgen Beyerer
Dr.-Ing. Christian Kuhnert
Prof. Dr.-Ing. Oliver Niggemann
(cid:0)1
7
18
26
36
46
58
66
77
87
97
107
Making Industrial Analytics work for Factory Automation Applications . 116
Markus Koester
Application of Reinforcement Learning in Production Planning and
Control of Cyber Physical Production Systems ...................... . 123
Andreas Kuhnle, Gisela Lanza
LoRaW an for Smarter Management of Water Network: From metering
to data analysis ........... ................. ..................... . 133
Jorge Fmnces-Chust, Joaquin Izquierdo, !del Montalvo
Machine Learning for Enhanced Waste Quantity
Reduction: Insights from the MONSOON
Industry 4.0 Project
Christian Beecks1,2, Shreekantha Devasya2, and Ruben Schlutter3
1 University of Mu¨nster, Germany
[email protected]
2 Fraunhofer Institute for Applied Information Technology FIT, Germany
{christian.beecks,shreekantha.devasya}@fit.fraunhofer.de
3 Kunststoff-Institut Lu¨denscheid, Germany
[email protected]
Abstract. Theproliferationofcyber-physicalsystemsandtheadvance-
ment of Internet of Things technologies have led to an explosive digiti-
zation of the industrial sector. Driven by the high-tech strategy of the
federal government in Germany, many manufacturers across all indus-
trysegmentsareacceleratingtheadoptionofcyber-physicalsystemand
Internet of Things technologies to manage and ultimately improve their
industrial production processes. In this work, we are focusing on the
EUfundedprojectMONSOON,whichisaconcreteexamplewherepro-
duction processes from different industrial sectors are to be optimized
via data-driven methodology. We show how the particular problem of
waste quantity reduction can be enhanced by means of machine learn-
ing. The results presented in this paper are useful for researchers and
practitioners in the field of machine learning for cyber-physical systems
in data-intensive Industry 4.0 domains.
Keywords: MachineLearning·PredictionModels·Cyber-physicalSys-
tems · Internet of Things · Industry 4.0
1 Introduction
The proliferation of cyber-physical systems and the advancement of Internet
of Things technologies have led to an explosive digitization of the industrial
sector. Driven by the high-tech strategy of the federal government in Germany,
many manufacturers across all industry segments are accelerating the adoption
of cyber-physical system and Internet of Things technologies to manage and
ultimately improve their industrial production processes.
The EU funded project MONSOON4 – MOdel-based coNtrol framework for
Site-wide OptimizatiON of data-intensive processes – is a concrete example
where production processes from different industrial sectors, namely process
4 http://www.spire2030.eu/monsoon
© The Author(s) 2019
J. Beyerer et al. (Eds.), Machine Learning for Cyber Physical Systems, Technologien
für die intelligente Automation 9, https://doi.org/10.1007/978-3-662-58485-9_1
(cid:0)
Fig.1. Parts and periphery of an injection molding machine (KIMW) [2].
industries from the sectors of aluminum and plastic, are to be optimized via
data-driven methodology.
In this work, we are focusing on a specific use case from the plastic industry.
We use sensor measurements provided by the cyber-physical systems of a real
production line producing coffee capsules and aim to reduce the waste quantity,
i.e., the number of low-quality production cycles, in a data-driven way. To this
end, we model the problem of waste quantity reduction as a two-class classifica-
tionproblemandinvestigatedifferentfundamentalmachinelearningapproaches
for detecting and predicting low-quality production cycles. We evaluate the ap-
proaches on a data set from a real production line and compare them in terms
of classification accuracy.
The paper is structured as follows. In Section 2, we describe the production
process and the collected sensor measurements. In Section 3, we present our
classificationmethodologyanddiscusstheresults.InSection4,weconcludethis
paper with an outlook on future work.
2 Production Process and Sensor Measurements
OneparticularresearchfocusinthescopeoftheprojectMONSOONliesonthe
plasticsector,wherethemanufacturingofpolymermaterials(coffeecapsules)is
performed by the injection molding method. Injection molding is a manufactur-
ing process that produces plastic parts by injecting raw material into a mold.
The process first heats the raw material, then closes the mold and injects the
hot plastic. After the holding pressure phase and the cooling phase the mold
is opened again and the plastic parts, i.e., coffee capsules in our scenario, are
extracted. In this way, each injection molding cycle produces one or multiple
