Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., & Schindler, A. (2018). PriMa-X: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning. In L. Wang (Ed.), 51st CIRP Conference on Manufacturing Systems (pp. 1039–1044). Elsevier BV. https://doi.org/10.1016/j.procir.2018.03.280
E330-02-1 - Forschungsgruppe Smart and Knowledge Based Maintenance E330-02 - Forschungsbereich Betriebstechnik, Systemplanung und Facility Management
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Erschienen in:
51st CIRP Conference on Manufacturing Systems
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Band:
72
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Datum (veröffentlicht):
2018
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Veranstaltungsname:
51st CIRP Conference on Manufacturing Systems
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Veranstaltungszeitraum:
16-Mai-2018 - 18-Mai-2018
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Veranstaltungsort:
Stockholm, EU
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Umfang:
6
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Verlag:
Elsevier BV
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Peer Reviewed:
Ja
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Keywords:
General Materials Science; reference model; cyber physcial production systems; prescriptive maintenance; data science; maturity
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Abstract:
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.
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Forschungsschwerpunkte:
Digital Transformation in Manufacturing: 50% Automation and Robotics: 50%