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Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning
Philotech GmbH, Buxtehude, Germany.
2013 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 12, 5021-5026 p.Article in journal (Refereed) Published
Abstract [en]

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 40, no 12, 5021-5026 p.
Keyword [en]
Decision tree; Forecasting; Expert system; Machine learning; Time series; Maintenance; Genetic algorithm
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-105842DOI: 10.1016/j.eswa.2013.03.025OAI: oai:DiVA.org:liu-105842DiVA: diva2:711200
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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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • Other style
More styles
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  • Other locale
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Output format
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