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

1 Citation (Scopus)

19 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

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applsci-10-08348-v2Final published version, 311 KBLicence: CC BY

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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.",

author = "Bram Ton and Rob Basten and John Bolte and Jan Braaksma and Bucchianico, {Alessandro Di} and Calseyde, {Philippe van de} and Frank Grooteman and Tom Heskes and Nils Jansen and Wouter Teeuw and Tiedo Tinga and Mari{\"e}lle Stoelinga",

note = "Funding Information: The PrimaVera project includes leading industrial partners from three major sectors of the Dutch economy: infrastructure, high-tech and maritime. The project has been awarded a grant of five million euros in funding from the Dutch Research Council (NWO) and co-funding from the participating consortium members. The project has a duration of five years. Funding Information: Funding: This research was partly funded by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) grant number NWA.1160.18.238 and co-funding from the participating consortium members. The Article Processing Charges (APC) was funded by the University of Twente. Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

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doi = "10.3390/app10238348",

language = "English",

volume = "10",

pages = "1--19",

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PrimaVera : Synergising Predictive Maintenance. / Ton, Bram; Basten, Rob; Bolte, John; Braaksma, Jan; Bucchianico, Alessandro Di; Calseyde, Philippe van de; Grooteman, Frank; Heskes, Tom; Jansen, Nils; Teeuw, Wouter; Tinga, Tiedo ; Stoelinga, Mariëlle.

In: Applied Sciences, Vol. 10, No. 23, 8348, 24.11.2020, p. 1-19.

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

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T1 - PrimaVera

T2 - Synergising Predictive Maintenance

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

N1 - Funding Information: The PrimaVera project includes leading industrial partners from three major sectors of the Dutch economy: infrastructure, high-tech and maritime. The project has been awarded a grant of five million euros in funding from the Dutch Research Council (NWO) and co-funding from the participating consortium members. The project has a duration of five years. Funding Information: Funding: This research was partly funded by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) grant number NWA.1160.18.238 and co-funding from the participating consortium members. The Article Processing Charges (APC) was funded by the University of Twente. Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/11/24

Y1 - 2020/11/24

N2 - 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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PrimaVera: Synergising Predictive Maintenance

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