PrimaVera: Synergising Predictive Maintenance

Bram Ton, Rob Basten, John Bolte, Jan Braaksma, Alessandro Di Bucchianico, Philippe van de Calseyde, Frank Grooteman, Tom Heskes, Nils Jansen, Wouter Teeuw, Tiedo Tinga, Marielle Stoelinga*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

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.
Original languageEnglish
Article number8348
Number of pages19
JournalApplied Sciences
Volume10
Issue number23
DOIs
Publication statusPublished - 24 Nov 2020

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