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Fuzzy Condition Monitoring of Recirculation Fans and Filters
Aircraft Design and Systems Group, Aero Hamburg University of Applied Sciences, Hamburg, Germany.
Aircraft Design and Systems Group, Aero Hamburg University of Applied Sciences, Hamburg, Germany.
2011 (English)In: Aeronautical Journal, ISSN 0001-9240, Vol. 2, no 1-4, 81-87 p.Article in journal (Refereed) Published
Abstract [en]

A reliable condition monitoring is needed 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 output similarity values for decision trees. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. Performed experiments showed that the concept is reliable and it also works with decision tree forests to increase the classification accuracy.

Place, publisher, year, edition, pages
Vienna: Springer, 2011. Vol. 2, no 1-4, 81-87 p.
Keyword [en]
Decision Trees, Fuzzy Decision Tree Evaluation, Air Conditioning, A340
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-105841DOI: 10.1007/s13272-011-0021-9OAI: oai:DiVA.org:liu-105841DiVA: diva2:711197
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Predictive Health Monitoring for Aircraft Systems using Decision Trees
Open this publication in new window or tab >>Predictive Health Monitoring for Aircraft Systems using Decision Trees
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 79 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1655
Keyword
Condition Monitoring, Condition Prediction, Failure Prediction, Decision Trees, Genetic Algorithm, Fuzzy Decision Tree Evaluation, System Monitoring, Aircraft Health Monitoring
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105843 (URN)LIU-TEK-LIC-2014:88 (Local ID)978-91-7519-346-5 (ISBN)LIU-TEK-LIC-2014:88 (Archive number)LIU-TEK-LIC-2014:88 (OAI)
Presentation
2014-04-11, A38, Hus A, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2014-04-09 Created: 2014-04-09 Last updated: 2015-07-10Bibliographically approved

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Citation style
  • apa
  • harvard1
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  • vancouver
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  • Other style
More styles
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  • Other locale
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