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 journalArticleAcademicpeer-review

9 Citations (Scopus)
61 Downloads (Pure)


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
Pages (from-to)1-19
Number of pages19
JournalApplied Sciences
Issue number23
Publication statusPublished - 24 Nov 2020


  • Predictive maintenance
  • Case studies
  • Interdisciplinary research
  • Process model


Dive into the research topics of 'PrimaVera: Synergising Predictive Maintenance'. Together they form a unique fingerprint.

Cite this