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; Mariëlle Stoelinga

doi:10.3390/app10238348

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, Mariëlle Stoelinga^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

15 Citations (Scopus)

133 Downloads (Pure)

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 language	English
Article number	8348
Pages (from-to)	1-19
Number of pages	19
Journal	Applied Sciences
Volume	10
Issue number	23
DOIs	https://doi.org/10.3390/app10238348
Publication status	Published - 24 Nov 2020

Keywords

Predictive maintenance
Case studies
Interdisciplinary research
Process model

Access to Document

10.3390/app10238348Licence: CC BY

ArticleFinal published version, 311 KBLicence: CC BY

15 Citations
1 PhD Thesis - Research external, graduation UT

Point taken: Translating the physical rail domain to cyber space using point clouds from mobile laser scanning
Ton, B., 2024, Enschede: University of Twente. 140 p.
Research output: Thesis › PhD Thesis - Research external, graduation UT

Open Access
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Cite this

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title = "PrimaVera: Synergising Predictive Maintenance",

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.",

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AU - Ton, Bram

AU - Basten, Rob

AU - Bolte, John

AU - Braaksma, Jan

AU - Bucchianico, Alessandro Di

AU - Calseyde, Philippe van de

AU - Grooteman, Frank

AU - Heskes, Tom

AU - Jansen, Nils

AU - Teeuw, Wouter

AU - Tinga, Tiedo

AU - Stoelinga, Mariëlle

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KW - Interdisciplinary research

KW - Process model

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PrimaVera: Synergising Predictive Maintenance

Abstract

Keywords

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Research output

Point taken: Translating the physical rail domain to cyber space using point clouds from mobile laser scanning

Cite this