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Datadrivna underhållsstrategier inom tillverkningsindustrier
University West, School of Business, Economics and IT, Divison of Informatics.
University West, School of Business, Economics and IT, Divison of Informatics.
2018 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The ever-increasing competition on the market in the manufacturing industry places new demands on the manufacturing industry to streamline its production. Maintaining the production operations for manufacturing industry is important in order to remain competitive. If a machine stops working, it may cause major costs in both repair and downtime. Driving a machine until it breaks before performing maintenance is not a sustainable maintenance work, the machine must be serviced before the error occurs. Scheduled maintenance can make improvement to this, but may also be resource-intensive. The machines need to be constantly monitored and maintenance should be performed when monitoring systems show trends that the machine condition deteriorates. This essay aims at studying the phenomenon of ”Predictive Maintenance” to see what opportunities advanced data collection and analysis technologies have for a manufacturing industry. The essay was conducted through interviews at the company GKN in Trollhättan, which is active in manufacturing for aircraft engines and other air components. The result has also been compared with previous research in the field, which describes computer-driven maintenance work techniques. This was to find out how a manufacturing industry can work proactively with data-driven maintenance strategies to prevent, in time, production failure. The result shows that there are advanced technologies that can send data using sensor technology, then use complex algorithms that analyze the sensor data to prevent production errors occurring. Applying the techniques to GKN is likely to increase their proactive possibilities, but the results from the GKN interviews indicate that there are more challenges than choosing a system for preventive maintenance. It was found in the interviews that there is a lack of communication in the need for a system solution, that the knowledge of the machines is limited to the operators and that the studied company lacks experience in analyzing data.

Place, publisher, year, edition, pages
2018. , p. 61
Keywords [en]
Predictive Maintenance, Internet of Things, Machine Learning, Condition Based Maintenance, Condition Monitoring, Machine-2-machine
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hv:diva-12739Local ID: EIC501OAI: oai:DiVA.org:hv-12739DiVA, id: diva2:1232137
Subject / course
Informatics
Educational program
Systemutveckling - IT och samhälle
Supervisors
Examiners
Available from: 2018-07-23 Created: 2018-07-10 Last updated: 2018-07-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf