A literature review framework and open research challenges for predictive maintenance in industry 4.0

  • Production issues at Volkswagen in 2016 led to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present an overview of the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literature survey, we present a classification of predictive maintenance in the context of Industry 4.0 based on 249 publications. Additionally, we discuss identified challenges, i.e., complexity issues, as well as missing benchmark datasets that are relevant for production and the integration of machine learning.

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Metadaten
Author:Lukas Meitz, Julia Senge, Tim Wagenhals, Thorsten Schöler, Jörg HähnerORCiDGND, Janick Edinger, Christian Krupitzer
URN:urn:nbn:de:bvb:384-opus4-1222398
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/122239
ISSN:0360-8352OPAC
Parent Title (English):Computers & Industrial Engineering
Publisher:Elsevier BV
Place of publication:Amsterdam
Type:Article
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/05/23
Volume:206
First Page:111193
DOI:https://doi.org/10.1016/j.cie.2025.111193
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Nachhaltigkeitsziele
Nachhaltigkeitsziele / Ziel 9 - Industrie, Innovation und Infrastruktur
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung