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T. Nemeth, F. Ansari, W. Sihn et al.:
"PriMa-X: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning";
Procedia CIRP, 72 (2018), S. 1039 - 1044.

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.

cyber physcial production systems; prescriptive maintenance; data science; reference model; maturity

http://dx.doi.org/10.1016/j.procir.2018.03.280

https://publik.tuwien.ac.at/files/publik_270903.pdf