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PriMa-X: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning

: Nemeth, Tanja; Ansari, Fazel; Sihn, Wilfried; Haslhofer, Bernhard; Schindler, Alexander

Volltext urn:nbn:de:0011-n-5206262 (640 KByte PDF)
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Erstellt am: 20.2.2020

Volltext ()

Procedia CIRP 72 (2018), S.1039-1044
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <51, 2018, Stockholm>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer Austria ()
Cyber-Physisches Produktionssystem; Data Science; Instandhaltung; maschinelles Lernen; maturity; Referenzmodell; reference model

The digital transformation already has a strong impact on manufacturing techniques and processes and requires novel data-driven maintenance strategies and models, which support prompt and effective decision-making. This poses new requirements, challenges and opportunities for securing and improving machine availability and process stability. This paper builds on the concept of prescriptive maintenance and proposes a reference model that (i) supports the implementation of a prescriptive maintenance strategy and the assessment of its maturity level, (ii) facilitates the integration of data-science methods for predicting future events, and (iii) identifies action fields to reach an enhanced target maturity state and thus higher prediction accuracy.