Production Engineering

, Volume 12, Issue 3–4, pp 525–533 | Cite as

A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers

  • Dominik KißkaltEmail author
  • Hans Fleischmann
  • Sven Kreitlein
  • Manuel Knott
  • Jörg Franke
Quality Assurance


Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device- and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the aggregation of control signals and process variables. Further challenges arise from the advance of flexible cyber-physical systems (CPS) and the industrial internet of things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. In this context, data-driven systems which are capable of unsupervised learning are promising approaches. The aim is the autonomous identification of significant process variables and patterns. This paper describes a machine learning approach for a condition and process monitoring system on the basis of pattern recognition within structure-borne noise of rotating cutting machinery. Process states are defined under application of non-negative matrix factorization (NMF). A production model is learned and deployed on the basis of Gaussian mixture models (GMM) and hidden Markov models (HMM) in a two stage process. Additionally a generic framework to ease the implementation of decentralized condition monitoring functionalities is given. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server. The approach is evaluated on the example of mill-turn centers.


Condition monitoring systems Smart factory Cyber-physical systems Machine learning Unsupervised learning Machine tools 



We would like to thank the Robert Bosch GmbH for providing access to its production facilities.


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Copyright information

© German Academic Society for Production Engineering (WGP) 2018

Authors and Affiliations

  1. 1.Institute for Factory Automation and Production SystemsFriedrich-Alexander-Universität Erlangen-Nuremberg (FAU)ErlangenGermany

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