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Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0001-9857-4317
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0002-9890-4918
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

Place, publisher, year, edition, pages
2012.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-17423OAI: oai:DiVA.org:mdh-17423DiVA, id: diva2:579754
Conference
The 2nd International Workshop and Congress on eMaintenance, 12-13 Dec 2012, Luleå, Sweden
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2013-12-03Bibliographically approved

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Xiong, NingFunk, PeterOlsson, Tomas

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CiteExportLink to record
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  • apa
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  • asciidoc
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