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1	Predictive Health Monitoring for Aircraft Systems using Decision Trees Gerdes, Mike January 2014 (has links) 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. Condition Monitoring Condition Prediction Failure Prediction Decision Trees Genetic Algorithm Fuzzy Decision Tree Evaluation System Monitoring Aircraft Health Monitoring
2	Health Monitoring for Aircraft Systems using Decision Trees and Genetic Evolution Gerdes, Mike January 2019 (has links) (PDF) Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process. ddc:620 info:eu-repo/classification/ddc/629.13 Luftfahrt Luftfahrzeug Instandhaltung Maschinelles Lernen Aeronautics Airplanes Decision trees Genetic algorithms 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

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Predictive Health Monitoring for Aircraft Systems using Decision Trees

Health Monitoring for Aircraft Systems using Decision Trees and Genetic Evolution