Log-based predictive maintenance in discrete parts manufacturing

Clemens Gutschi, Nikolaus Furian, Dietmar Neubacher

Research output: Contribution to conferenceAbstractpeer-review


The performance of discrete parts manufacturing systems is heavily influenced by unplanned machine breakdowns. Predictive maintenance allows for the conversation of unplanned machine breakdowns to scheduled corrective maintenance actions. We present a data-driven log-based approach for estimating the probability of machine breakdowns during specified time intervals in the future. Machine learning algorithms are utilized for a specific use-case which is based on real-world data-sets including machine log messages, event logs and operational information. We will present an overview on predictive maintenance strategies as well as applied data-preparation, feature-engineering and machine learning methods for estimating the remaining useful lifetime. The comparison of two different methods for RUL-estimation is indicating that machine failures can be predicted up to 168 hours in advance with promising precision and hit-rate.
Translated title of the contributionLog-based predictive maintenance in discrete parts manufacturing
Original languageEnglish
Publication statusPublished - Jun 2019
Event30th European Conference on Operational Research: 30th European Conference on Operational Research ​ - University College Dublin, Dublin, Ireland
Duration: 23 Jun 201926 Jun 2019


Conference30th European Conference on Operational Research
Abbreviated titleEURO 2019
Internet address


  • predictive analytics
  • Predictive maintenance
  • random forest
  • ensemble prediction
  • Machine learning
  • Probability of failure estimation
  • maintenance
  • Maintenance Management
  • data mining
  • feature engineering
  • feature selection
  • Remaining useful life
  • Ensemble prediction
  • Random forrest
  • condition monitoring

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research
  • Control and Systems Engineering

Fields of Expertise

  • Mobility & Production


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