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Key data quality pitfalls for condition based maintenance
School of Science Aalto University P.O. Box 15400, FI-00076 Aalto, Espoo, Finland.ORCID iD: 0000-0002-8078-5172
2017 (English)In: 2017 2nd International Conference on System Reliability and Safety (ICSRS), IEEE, 2017, p. 474-480Conference paper, Published paper (Refereed)
Abstract [en]

In today's competitive and fluctuating market, original equipment manufacturers (OEMs) must be able to offer aftersales services along with their products, such as condition based maintenance, extended warranty services etc. Condition based maintenance requires detailed understanding about products' operational behaviour, to detect problems before they occur, and react accordingly. Typically, Condition based maintenance consists of data collection, data analysis, and maintenance decision stages. Within this context, data quality is one of the key drivers in the knowledge acquisition process since poor data quality impacts the downstream maintenance processes, and reciprocally, high data quality will foster good decision making. The prospect of new business opportunities and better services to customers encourages companies to collect large amounts of data that have been generated in different stages of product lifecycle. Despite of availability of data, as well as advanced statistical and analytical tools, companies are still struggling to provide effective service by reducing maintenance cost and improving uptime. This paper highlights data related pitfalls that hinder organisations to improve maintenance services. These pitfalls are based on case studies of two globally operating Finnish manufacturing companies where maintenance is one of the major streams of income.

Place, publisher, year, edition, pages
IEEE, 2017. p. 474-480
Keywords [en]
Maintenance engineering, Data integrity, Companies, Reliability, Data models, Manufacturing
National Category
Information Systems
Identifiers
URN: urn:nbn:se:umu:diva-154889DOI: 10.1109/ICSRS.2017.8272868ISBN: 978-1-5386-3322-9 (electronic)OAI: oai:DiVA.org:umu-154889DiVA, id: diva2:1275001
Conference
2nd IEEE International Conference on System Reliability and Safety (ICSRS), Milan, Italy, Dec 20-22, 2017
Funder
EU, Horizon 2020, 688203Available from: 2019-01-04 Created: 2019-01-04 Last updated: 2019-06-18Bibliographically approved

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Främling, Kary

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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