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Prognostic Hybrid Modelling from Data Fusion on Machine Tools
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-1938-0985
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Department of Information Engineering, University of Florence.
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2016 (English)In: Measurement, ISSN 1536-6367, E-ISSN 1536-6359Article in journal (Other (popular science, discussion, etc.)) Accepted
Abstract [en]

This paper proposes an enhancement of remaining useful life (RUL) prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

Place, publisher, year, edition, pages
2016.
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-9859Local ID: 88d55f45-4bf0-46a9-b53a-7c1dba2dc2dfOAI: oai:DiVA.org:ltu-9859DiVA, id: diva2:982797
Note

Upprättat; 2015; 20151130 (aditha)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-04-26

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Galar, DiegoThaduri, AdithyaSimon, Victor

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