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Fuzzy condition monitoring of recirculation fans and filters
Aero - Aircraft Design and Systems Group, Hamburg University of Applied Sciences, Hamburg, Germany.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4107-0991
2016 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, 469-479 p.Article in journal (Refereed) Published
Abstract [en]

A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

Place, publisher, year, edition, pages
2016. Vol. 7, no 4, 469-479 p.
Keyword [en]
Fuzzy decision trees, Post-fuzzyfication, Condition monitoring, Aircraft
URN: urn:nbn:se:his:diva-13176DOI: 10.1007/s13198-016-0535-yISI: 000387346100010OAI: oai:DiVA.org:his-13176DiVA: diva2:1051391
Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2017-01-20

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Galar, Diego
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