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Towards data mining based decision support in manufacturing maintenance
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-2545-7838
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-8906-630X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 261-265Article in journal (Refereed) Published
Abstract [en]

The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 72, p. 261-265
Keywords [en]
Maintenance, Decision Support System, Data Mining, Classification Methods, Knowledge Extraction
National Category
Information Systems
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-15901DOI: 10.1016/j.procir.2018.03.076Scopus ID: 2-s2.0-85049600893OAI: oai:DiVA.org:his-15901DiVA, id: diva2:1229550
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-07-01 Created: 2018-07-01 Last updated: 2018-10-31Bibliographically approved

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Gandhi, KanikaSchmidt, BernardNg, Amos H. C.

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