Automatic generation of improvement suggestions for legacy, PLC controlled manufacturing equipment utilizing machine learning

Wolfgang Koehler, Yanguo Jing

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The manufacturing industry and, for this research, the automotive manufacturing industry specifically, is always on the lookout for opportunities to improve production throughput with a minimum of investment. Identifying these opportunities often requires the observation of the current production process by experts. This paper is the continuation of the previous work ’Automated, Nomenclature Based Data Point Selection for Industrial Event Log Generation’. One of its aims is to provide strategies that can be used to pre-process an in-depth, slightly flawed industrial equipment log to allow for further analysis. The pre-processing is achieved by identifying the flaws, removing the non-value added events and a heuristic methodology to cluster the log into individual sequences. Expert knowledge then is encoded into engineering features to extend the log matrix and prepare it for machine learning model generation for identification of the complete cases. To derive value from the available data, the sequences are plotted into Gantt charts, and eight hypotheses are introduced that allow for automated annotations within this chart to highlight potential areas of improvement. Application of the framework to real life logs, obtained from stations considered bottlenecks within the evaluated automotive body shop, lead to the discovery of improvement potential between two and twelve seconds per cycle.
Original languageEnglish
Title of host publicationMachine learning for cyber physical systems
Subtitle of host publicationSelected papers from the International Conference ML4CPS 2020
EditorsJürgen Beyerer, Alexander Maier, Oliver Niggemann
Place of PublicationBerlin
PublisherSpringer Nature
Number of pages10
ISBN (Electronic)9783662627464
ISBN (Print)9783662627457
Publication statusPublished - 24 Dec 2020
Externally publishedYes
Event5th Machine Learning for Cyber Physical Systems Conference: Machine Learning in Production - Fraunhofer-Forum, Berlin, Germany
Duration: 12 Mar 202013 Mar 2020

Publication series

NameTechnologien für die intelligente Automation
PublisherSpringer Vieweg
ISSN (Print)2522-8579
ISSN (Electronic)2522-8587

Academic conference

Academic conference5th Machine Learning for Cyber Physical Systems Conference
Abbreviated title5th ML4CPS Conference


  • Industrial Logs
  • Process Mining
  • Case Clustering


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