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Case-based reasoning supports fault diagnosis using sensor information
Mälardalen University, School of Innovation, Design and Engineering. (IS (Embedded Systems))ORCID iD: 0000-0001-9857-4317
Mälardalen University, School of Innovation, Design and Engineering. (IS (Embedded Systems))ORCID iD: 0000-0002-9890-4918
Mälardalen University, School of Innovation, Design and Engineering. (IS (Embedded Systems))ORCID iD: 0000-0002-5562-1424
2013 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 16, no 4, p. 25-30Article in journal (Refereed) Published
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
2013. Vol. 16, no 4, p. 25-30
Keyword [en]
Fault Diagnostics, Case-based Reasoning, Sensors, Signal Processing, Feature Extraction, Crack Detection, Process Monitoring, ArtificialIntelligence, Knowledge Discovery, Case Retrieval
National Category
Engineering and Technology Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-22258Scopus ID: 2-s2.0-84940274783OAI: oai:DiVA.org:mdh-22258DiVA, id: diva2:661139
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2018-01-11Bibliographically approved

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Xiong, NingOlsson, TomasFunk, Peter

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