Improving Maintenance Processes with Data Science

Prill, Dorian; Kranzer, Simon; Merz, Robert

doi:10.1007/978-3-658-19287-7_15

Improving Maintenance Processes with Data Science

How Machine Learning Opens Up New Possibilities

Dorian Prill⁴,
Simon Kranzer⁴ &
Robert Merz⁴

Conference paper
First Online: 16 September 2017

4832 Accesses
1 Citations

Zusammenfassung

In this presentation we briefly describe potential benefits of using data analysis methods to improve maintenance processes. After a short introduction to an automated, multi-step maintenance process and a survey of the state of data in industry, we explain, how selected data analysis methods can be used to improve maintenance demand detection

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Literatur

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Authors and Affiliations

Department Information Technology & Systems Management, Salzburg University of Applied Sciences, Salzburg, Österreich
Dorian Prill, Simon Kranzer & Robert Merz

Authors

Dorian Prill
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Simon Kranzer
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Robert Merz
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Corresponding author

Correspondence to Dorian Prill .

Editor information

Editors and Affiliations

Fachhochschule Salzburg, Puch/Salzburg, Austria
Peter Haber
its Informationstechnik & System-Management, Fachhochschule Salzburg, Puch/Salzburg, Austria
Thomas Lampoltshammer
Informationstechnik & System-Management, Fachhochschule Salzburg, Puch/Salzburg, Austria
Manfred Mayr

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Prill, D., Kranzer, S., Merz, R. (2017). Improving Maintenance Processes with Data Science. In: Haber, P., Lampoltshammer, T., Mayr, M. (eds) Data Science – Analytics and Applications. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-19287-7_15

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DOI: https://doi.org/10.1007/978-3-658-19287-7_15
Published: 16 September 2017
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-19286-0
Online ISBN: 978-3-658-19287-7
eBook Packages: Computer Science and Engineering (German Language)

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