@article{17ba775fd8e941efa9a7f914e6fce1ca,
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.",
keywords = "Predictive maintenance, Case studies, Interdisciplinary research, Process model",
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.",
year = "2020",
month = nov,
day = "24",
doi = "10.3390/app10238348",
language = "English",
volume = "10",
pages = "1--19",
journal = "Applied Sciences",
issn = "1454-5101",
publisher = "Politechnica University of Bucharest",
number = "23",
}