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Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations
Article

PrimaVera: Synergising Predictive Maintenance

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Saxion University of Applied Sciences, 7513 AB Enschede, The Netherlands
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Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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The Hague University of Applied Sciences, 2521 EN Den Haag, The Netherlands
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Department of Design, University of Twente, Production and Management, 7522 NB Enschede, The Netherlands
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Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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Royal Netherlands Aerospace Centre, 1059 CM Amsterdam, The Netherlands
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Institute for Computing and Information Sciences, Radboud University, 6525 XZ Nijmegen, The Netherlands
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Department of Mechanics of Solids, Surfaces & Systems, University of Twente, 7522 NB Enschede, The Netherlands
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Formal Methods and Tools, University of Twente, 7522 NB Enschede, The Netherlands
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(23), 8348; https://doi.org/10.3390/app10238348
Received: 31 July 2020 / Revised: 5 November 2020 / Accepted: 19 November 2020 / Published: 24 November 2020
(This article belongs to the Special Issue Overcoming the Obstacles to Predictive Maintenance)
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. View Full-Text
Keywords: predictive maintenance; process model; interdisciplinary research; case studies predictive maintenance; process model; interdisciplinary research; case studies
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MDPI and ACS Style

Ton, B.; Basten, R.; Bolte, J.; Braaksma, J.; Di Bucchianico, A.; van de Calseyde, P.; Grooteman, F.; Heskes, T.; Jansen, N.; Teeuw, W.; Tinga, T.; Stoelinga, M. PrimaVera: Synergising Predictive Maintenance. Appl. Sci. 2020, 10, 8348. https://doi.org/10.3390/app10238348

AMA Style

Ton B, Basten R, Bolte J, Braaksma J, Di Bucchianico A, van de Calseyde P, Grooteman F, Heskes T, Jansen N, Teeuw W, Tinga T, Stoelinga M. PrimaVera: Synergising Predictive Maintenance. Applied Sciences. 2020; 10(23):8348. https://doi.org/10.3390/app10238348

Chicago/Turabian Style

Ton, Bram; Basten, Rob; Bolte, John; Braaksma, Jan; Di Bucchianico, Alessandro; van de Calseyde, Philippe; Grooteman, Frank; Heskes, Tom; Jansen, Nils; Teeuw, Wouter; Tinga, Tiedo; Stoelinga, Mariëlle. 2020. "PrimaVera: Synergising Predictive Maintenance" Appl. Sci. 10, no. 23: 8348. https://doi.org/10.3390/app10238348

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