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@ARTICLE{Knig:810083,
      author       = {König, Florian and Sous, Christopher and Ouald Chaib,
                      Achraf and Jacobs, Georg},
      title        = {{M}achine learning based anomaly detection and
                      classification of acoustic emission events for wear
                      monitoring in sliding bearing systems},
      journal      = {Tribology international},
      volume       = {155},
      issn         = {0301-679X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {RWTH-2021-00381},
      pages        = {106811},
      year         = {2021},
      note         = {Received 8 October 2020, Revised 28 November 2020, Accepted
                      30 November 2020, Available online 3 December 2020},
      cin          = {411710},
      ddc          = {660},
      cid          = {$I:(DE-82)411710_20190404$},
      pnm          = {PFSDS026 - Data science based wear prediction
                      (EXS-PF-PFSDS026) / ERS Prep Fund - Exploratory Research
                      Space: Prep Fund als Anschubfinanzierung zur Schließung
                      strategischer Lücken (EXS-PF) / EXS - Excellence Strategy
                      (EXS)},
      pid          = {G:(DE-82)EXS-PF-PFSDS026 / G:(DE-82)EXS-PF / G:(DE-82)EXS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000612137700002},
      doi          = {10.1016/j.triboint.2020.106811},
      url          = {https://publications.rwth-aachen.de/record/810083},
}