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Predictive Health Monitoring for Aircraft Systems using Decision Trees

Mike Gerdes

Abstract
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.

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Date:2014-04-11
Type of work: Licentiate Thesis
Advisor:Dieter Scholz, HAW Hamburg
Advisor:Petter Krus, Linköpings Universitet
Advisor:Bernhard Randerath, Airbus
Opponent:Diego Galar, Luleå Tekniska Universitet
Public Defence:2014-04-11, A38, Hus A, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Published by:Aircraft Design and Systems Group (AERO), Department of Automotive and Aeronautical Engineering, Hamburg University of Applied Sciences
ISBN (print):978-91-7519-346-5
Project:http://PAHMIR.ProfScholz.de pin
This work is part of:transparent pin for text alignment Digital Library - Projects & Theses - Prof. Dr. Scholz --- http://library.ProfScholz.de pin
 
PERSISTENT IDENTIFIER:
URN: https://nbn-resolving.org/urn:nbn:de:gbv:18302-aero2014-04-11 (to reach this page)
URN: https://nbn-resolving.org/urn:nbn:se:liu:diva-105843
DOI:https://doi.org/10.5281/zenodo.18100616
ARK:https://n2t.net/ark:/13960/s2h6mhc4fq1
HANDLE:http://hdl.handle.net/20.500.12738/1093
Associated research data:none
URLs registered with URN: Show all links associated with this text!
 
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Keywords, German (GND): Luftfahrt,   Luftfahrzeug,   Instandhaltung,   Maschinelles Lernen
Keywords, English (LCSH): Aeronautics,   Airplanes,   Decision trees,   Genetic algorithms
Keywords, free: Flugzeugsysteme, Wartung, Expert Systems, Machine Learning, Big Data, Pattern recognition systems, Condition Monitoring, Remaining Useful Life Prediction, Fuzzy Decision Tree Evaluation, System Monitoring, Aircraft Health Monitoring
DDC: 629.13,    629.133340423,    629.13437,    620.0046
RVK: ZO 7229

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CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0

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Any further request may be directed to:
Prof. Dr.-Ing. Dieter Scholz, MSME
E-Mail see: http://www.ProfScholz.de

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GERDES, Mike, 2014. Predictive Health Monitoring for Aircraft Systems using Decision Trees. Licentiate Thesis. Hamburg University of Applied Sciences, Aircraft Design and Systems Group (AERO). Available from: https://nbn-resolving.org/urn:nbn:de:gbv:18302-aero2014-04-11 [viewed YYYY-MM-DD].

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LAST UPDATE:  03 January 2026
AUTHOR:  Prof. Dr. Scholz
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