Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles

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@InProceedings{6351,
  author =       "Jan Zenisek and Michael Affenzeller and 
                 Josef Wolfartsberger and Mathias Silmbroth and 
                 Christoph Sievi and Aziz Huskic and Herbert Jodlbauer",
  title =        "Sliding Window Symbolic Regression for Predictive
                 Maintenance using Model Ensembles",
  booktitle =    "Computer Aided Systems Theory, EUROCAST 2017",
  year =         "2017",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "10671",
  series =       "Lecture Notes in Computer Science",
  pages =        "481--488",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-74718-7",
  URL =          "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_58",
  DOI =          "doi:10.1007/978-3-319-74718-7_58",
  abstract =     "Predictive Maintenance (PdM) is among the trending
                 topics in the current Industry 4.0 movement and hence,
                 intensively investigated. It aims at sophisticated
                 scheduling of maintenance, mostly in the area of
                 industrial production plants. The idea behind PdM is
                 that, instead of following fixed intervals, service
                 actions could be planned based upon the monitored
                 system condition in order to prevent outages, which
                 leads to less redundant maintenance procedures and less
                 necessary overhauls. In this work we will present a
                 method to analyse a continuous stream of data, which
                 describes a system's condition progressively.
                 Therefore, we motivate the employment of symbolic
                 regression ensemble models and introduce a
                 sliding-window based algorithm for their evaluation and
                 the detection of stable and changing system states.",
  notes =        "Published 2018?",
}

Genetic Programming entries for Jan Zenisek Michael Affenzeller Josef Wolfartsberger Mathias Silmbroth Christoph Sievi Aziz Huskic Herbert Jodlbauer

Citations