Health monitoring for aircraft systems using decision trees and genetic evolution - Mike Gerdes (Luleå University of Technology, 2019)
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.
Keywords: Luftfahrt; Luftfahrzeug; Instandhaltung; Maschinelles Lernen; Flugzeugsysteme; Wartung; Aeronautics; Airplanes; Decision Trees; Genetic Algorithms; Expert Systems; Machine Learning; Big Data; Pattern Recognition Systems; Condition Monitoring; Remaining Useful Life Prediction; Fuzzy Decision Tree Evaluation; System Monitoring; Aircraft Health Monitoring; Feature Extraction; Feature Selection; Data Driven; Health Prognostic; Knowledge Based System; Supervised Learning; Data-Driven Predictive Health Monitoring; Health Indicators