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A method for implementation of machine learning solutions for predictive maintenance in small and medium sized enterprises

  • In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.

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Metadaten
Name:Welte, Rebecca; Estler, Manfred; Lucke, Dominik
URN:urn:nbn:de:bsz:rt2-opus4-28125
DOI:https://doi.org/10.1016/j.procir.2020.04.052
ISSN:2212-8271
Erschienen in:Procedia CIRP
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of Publication:2020
Tag:implementation method; machine learning; predictive maintenance
Volume:93
Pagenumber:6
First Page:909
Last Page:914
Dewey Decimal Classification:670 Industrielle und handwerkliche Fertigung
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International