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Predicting Target Events in Industrial Domains

  • Julio Borges
  • Martin A. Neumann
  • Christian Bauer
  • Yong Ding
  • Till Riedel
  • Michael Beigl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10358)

Abstract

In industrial environments, machine faults have a high impact on productivity due to the high costs it can cause. Machine generated event logs are a abundant source of information for understanding the causes and events that led to a critical event in the machine. In this work, we present a Sequence-Mining based technique to automatically extract sequential patterns of events from machine log data for understanding and predicting machine critical events. By experiments using real data with millions of log entries from over 150 industrial computer numerical control (CNC) cutting machines, we show how our technique can be successfully used for understanding the root causes of certain critical events, as well as for building monitors for predicting them long before they happen, outperforming existing techniques.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Julio Borges
    • 1
  • Martin A. Neumann
    • 1
  • Christian Bauer
    • 2
  • Yong Ding
    • 1
  • Till Riedel
    • 1
  • Michael Beigl
    • 1
  1. 1.TECOKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.TRUMPF Werkzeugmaschinen GmbH + Co. KGDitzingenGermany

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