PREDICTIVE MAINTENANCE FOR VERY EFFECTIVE ASSET MANAGEMENT
The vision of the PrimaVera project was to make predictive maintenance easier and more effective, thereby realizing its full potential: better system performance and higher availability at lower costs.
PROJECT MOTIVATION
Predictive Maintenance (PdM) refers to the use of data-driven analytics to optimize the availability of capital assets. It creates value by transforming data collected from intelligent systems into predictions about asset health, thereby preventing failures through just-in-time maintenance.
While many core building blocks of PdM—such as sensor technologies, failure prediction methods, and optimization techniques—are already available, existing solutions typically address only individual steps of the PdM cycle and are tailored to very specific settings. The overarching challenge of the PrimaVera project was to integrate these building blocks into a coherent, effective, and efficient framework to support optimal maintenance and asset management.
PROJECT CONSORTIUM
The PrimaVera project ran from 2020 to 2025. It was funded by the Dutch National Research Agenda (NWA) and carried out by a consortium consisting of:
- NLR Nederlands Lucht- en Ruimtevaartcentrum (Netherlands Aerospace Centre),
- NS Nederlandse Spoorwegen (Netherlands Railways),
- RNL Navy Royal Netherlands Navy
- RU Radboud University
- RWS Rijkswaterstaat
- TO2 institute Federation of Dutch institutes for applied research
- THUAS The Hague University of Applied Sciences
- TU/e Eindhoven University of Technology
- TTO Technology and Transfer Office
- UT University of Twente
- WDD Waterschap de Dommel (Water board de Dommel)
CONTRIBUTION OF THE ASSET MANAGEMENT AND MAINTENANCE ENGINEERNG CHAIR
Our team collaborated with the Eindhoven University of Technology (TU/e) and industrial partners including Rijkswaterstaat, NS, and World Class Maintenance (WCM) to investigate the organizational and human factors associated with PdM and to develop a decision-support tool for guiding PdM implementation. As part of this effort, the team created an online catalogue showcasing the PdM solutions and services developed within the PrimaVera project. In addition, a participatory methodology was designed to support the co-creation of strategic objectives for asset management capabilities in inter-organizational maintenance partnerships undergoing digital transformation.
AMME Team
- prof.dr. A.J.J. Braaksma (Jan)
- G. Barbieri PhD (Giacomo)
- dr. ir. B. van Oudenhoven (Bas)
- D. Sanchez Londono MSc (David)
- ir. J. Moerman (Jan-Jaap)
publications
- Bas Van Oudenhoven; David Sanchez-Londono; Giacomo Barbieri; Jan Braaksma: The Knowledge Product Catalogue as a Tool to Support Dissemination in Inter-Organizational Research Projects. In: Work in progress, 2026.
- Giacomo Barbieri; Catharina Ebbers; Jan Braaksma: A Participatory Methodology for the Co-Creation of Strategic Objectives in Inter-Organizational Maintenance Partnerships. In: Work in progress, 2026.
- van Oudenhoven, B., Demerouti, E., Basten, R., & Van de Calseyde, P. (2025). Preparing for predictive maintenance: Employee perspectives on job demands and resources before and after its implementation. Applied Ergonomics, 129, 104561.
- Van Oudenhoven, B., Van de Calseyde, P., Basten, R., & Demerouti, E. (2023). Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective. International Journal of Production Research, 61(22), 7846-7865.
- Ton, B., Basten, R., Bolte, J., Braaksma, J., Di Bucchianico, A., van de Calseyde, P., ... & Stoelinga, M. (2020). PrimaVera: Synergising predictive maintenance. Applied Sciences, 10(23), 8348.
