Overview of the CPS for Smart Factories Project: Deep Learning, Knowledge Acquisition, Anomaly Detection and Intelligent User Interfaces

  • Daniel SonntagEmail author
  • Sonja Zillner
  • Patrick van der Smagt
  • András Lörincz
Part of the Springer Series in Wireless Technology book series (SSWT)


Industry 4.0 factories become more and more complex with increased maintenance costs. Reducing costs by cyber-physical (CP) controllers should ensure the commercialization of the CPS for smart factory project results. We implement multi-adaptive CP controllers in the following domains: industrial robot arms, car manufacturing, steel industry, and assembly lines in general. The main objective is to implement such controllers for these application domains and let the industry partners provide feedback about the cost reduction potential. In this paper, we describe the technical infrastructure including deep learning and knowledge acquisition submodules, followed by anomaly detection modules and intelligent user interfaces in the IoT (Internet of Things) paradigm. In addition, we report on three concrete use case implementations of industrial robots and anomaly modeling, knowledge management and anomaly treatment in the steel domain, and anomaly detection in the energy domain.


Anomaly Detection Deep Learning Deep Neural Network Maintenance Process Seamless Integration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank our project partners in CPS for Smart Factories, funded by EIT Digital (EU, Horizon 2020). See


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Daniel Sonntag
    • 1
    Email author
  • Sonja Zillner
    • 2
  • Patrick van der Smagt
    • 3
  • András Lörincz
    • 4
  1. 1.DFKISaarbrückenGermany
  2. 2.SiemensMunichGermany
  3. 3.FortissTUMMunichGermany
  4. 4.ELTEBudapestHungary

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