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Key Data Quality Pitfalls for Condition Based Maintenance

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Madhikermi, Manik
dc.contributor.author Buda, Andrea
dc.contributor.author Dave, Bhargav
dc.contributor.author Främling, Kary
dc.date.accessioned 2018-08-21T13:45:07Z
dc.date.available 2018-08-21T13:45:07Z
dc.date.issued 2018
dc.identifier.citation Madhikermi , M , Buda , A , Dave , B & Främling , K 2018 , Key Data Quality Pitfalls for Condition Based Maintenance . in 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017 . IEEE , pp. 474-480 , International Conference on System Reliability and Safety , Milan , Italy , 20/12/2017 . https://doi.org/10.1109/ICSRS.2017.8272868 en
dc.identifier.isbn 978-1-5386-3322-9
dc.identifier.other PURE UUID: 6fd1a97e-3460-437a-9526-82c60c04dc57
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/6fd1a97e-3460-437a-9526-82c60c04dc57
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33506
dc.description | openaire: EC/H2020/688203/EU//BIoTope
dc.description.abstract 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. en
dc.format.extent 474-480
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/688203/EU//BIoTope
dc.relation.ispartof International Conference on System Reliability and Safety en
dc.relation.ispartofseries 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017 en
dc.rights restrictedAccess en
dc.title Key Data Quality Pitfalls for Condition Based Maintenance en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Adj. Prof. Främling Kary group
dc.subject.keyword condition based maintenance
dc.subject.keyword data analysis
dc.subject.keyword data quality
dc.subject.keyword data reliability
dc.subject.keyword after-sales service
dc.subject.keyword statistics
dc.identifier.urn URN:NBN:fi:aalto-201808214639
dc.identifier.doi 10.1109/ICSRS.2017.8272868

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