PrimaVera: Synergising Predictive Maintenance
Authors/Creators
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Ton, Bram
(Researcher)1, 2
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Basten, Rob
(Researcher)3
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Bolte, John
(Researcher)4, 5
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Braaksma, Jan
(Researcher)1
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Di Bucchianico, Alessandro
(Researcher)
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van de Calseyde, Philippe
(Researcher)3
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Grooteman, Frank
(Researcher)6
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Heskes, Tom
(Researcher)7
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Jansen, Nils
(Researcher)7
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Teeuw, Wouter
(Researcher)2
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Tinga, Tiedo
(Researcher)1, 8
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Stoelinga, Marielle
(Project leader)1, 7
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1.
University of Twente
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2.
Saxion University of Applied Sciences
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3.
Eindhoven University of Technology
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4.
National Institute for Public Health and the Environment
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5.
The Hague University of Applied Sciences
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6.
Netherlands Aerospace Centre
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7.
Radboud University Nijmegen
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8.
Netherlands Defence Academy
Description
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.
Files
applsci-10-08348-v2.pdf
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