Abstract
In our work we used the capability of one-class support vector machine (SVM) method to develop a novel one-class classification approach. Algorithm is designed and tested, aimed for fault detection in complex technological systems, such as aircraft. The main objective of this project was to create an algorithm responsible for collecting and analyzing the data since the launch of an aircraft engine. Data can be transferred from a variety of sensors that are responsible for the speed, oil temperature and etc. In order to provide high generalization level and sufficient learning data sets an incremental algorithm is considered. The proposed method analyzes both “positive”/“normal” and “negative”/ “abnormal” examples. However, overall model structure is based on one-class classification paradigm. Modified SVM-base outlier detection method is verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the Western European and Russia. The test results are presented in the final part of the article.
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Kolev, D., Suvorov, M., Morozov, E., Markarian, G., Angelov, P. (2015). Incremental Anomaly Identification in Flight Data Analysis by Adapted One-Class SVM Method. In: Koprinkova-Hristova, P., Mladenov, V., Kasabov, N. (eds) Artificial Neural Networks. Springer Series in Bio-/Neuroinformatics, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-09903-3_18
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DOI: https://doi.org/10.1007/978-3-319-09903-3_18
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