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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

https://doi.org/10.3390/app10238348

LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/

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OrganisatieDe Haagse Hogeschool
AfdelingFaculteit Technologie, Innovatie & Samenleving
LectoraatLectoraat Smart Sensor Systems
Gepubliceerd inApplied Sciences MDPI, Basel, Zwitserland, Vol. 10, Uitgave: 23, Pagina: 8348
Datum2020-11-24
TypeArtikel
DOI10.3390/app10238348
TaalEngels

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