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Decision support systems in maintenance: fusion of business data and physical data
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7474-2723
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
University of Zaragoza.
2012 (English)In: Condition Monitor, ISSN 0268-8050, no 299, p. 7-12Article in journal (Other academic) Published
Abstract [en]

Maintenance optimization is critical for enhancing the effectiveness of asset management system. Maintenance effectiveness depends on the quality,timeliness, accuracy and completeness of information related to machinedegradation state, based on which decisions are made. With emergenceof intelligent sensors to measure and monitor the health state of the component and gradual implementation of information and communication technologies (lCT) in organizations conceptualization and implementation of e-maintenance is turning into a reality. While e-maintenance shows a lot of promise, seamless integration of ICT into the industrial environment remains a challenge. Computer Maintenance Management Systems (CMMS) are the core of traditional maintenance record-keeping practices and often facilitate the usage of textual descriptions of faults and actions performed on a vehicle. Recently developed Condition Monitoring Systems (CM) are capable of directly monitoring asset componentsparameters; however, attempts to link observed CMMS events to CM sensormeasurements have been fairly limited in their approach and scalability.In this article, we present our approach for addressing this predicament.We argue that understanding the requirements and constraints in conjunction- from maintenance and ICT perspectives - is necessary. We identifythe issues that need be addressed for achieving successful e-maintenanceproviding a new theoretical process for integrating the two disparatedata types.

Place, publisher, year, edition, pages
2012. no 299, p. 7-12
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-3867Local ID: 1b77c285-63ca-454d-8564-e53eae9783d6OAI: oai:DiVA.org:ltu-3867DiVA, id: diva2:976729
Note

Godkänd; 2012; 20131209 (diegal)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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Parida, AdityaGalar, Diego

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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