Comparing machine learning methods on concept drift detection for Predictive Maintenance

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Abstract

In this work we present a comparison of various machine learning algorithms with the objective of detecting concept drifts in data streams characteristical for condition monitoring of industrial production plants. Although there is a fair number of contributions employing machine learning algorithms in related fields such as traditional time series forecasting or concept drift learning, data sets with sensor streams from a production plant are rarely covered. This work aims at shedding some light on the matter of how efficient the depicted algorithms perform on concept drift detection to pave the way for Predictive Maintenance (PdM) and which intermediate data processing steps therefore might be beneficial.
OriginalspracheEnglisch
Titel30th European Modeling and Simulation Symposium, EMSS 2018
Redakteure/-innenYuri Merkuryev, Miquel Angel Piera, Francesco Longo, Agostino G. Bruzzone, Michael Affenzeller, Emilio Jimenez
Herausgeber (Verlag)DIME UNIVERSITY OF GENOA
Seiten115-122
Seitenumfang8
ISBN (elektronisch)9788885741065
ISBN (Print)978-88-85741-03-4
PublikationsstatusVeröffentlicht - 2018
VeranstaltungThe 30th European Modeling & Simulation Symposium EMSS 2018 - Budapest, Hungary, Ungarn
Dauer: 17 Sep 201819 Sep 2018
http://www.msc-les.org/conf/emss2018/index.html

Publikationsreihe

Name30th European Modeling and Simulation Symposium, EMSS 2018

Konferenz

KonferenzThe 30th European Modeling & Simulation Symposium EMSS 2018
Land/GebietUngarn
OrtBudapest, Hungary
Zeitraum17.09.201819.09.2018
Internetadresse

Schlagwörter

  • Machine Learning
  • Predictive Maintenance
  • Concept Drift Detection
  • Time Series Regression

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