Incremental anomaly identification by adapted SVM method

Suvorov, Michail and Ivliev, Sergey and Markarian, Garik and Kolev, Denis and Zvikhachevskiy, Dmitry and Angelov, Plamen (2013) Incremental anomaly identification by adapted SVM method. In: International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013. IEEE, Piscataway, N.J., pp. 1-8. ISBN 9781467361286

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 within the project SVETLANA 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, oxygen level etc. In order to apply real time (in flight) application a recursive learning algorithm is proposed. The proposed method analyzes both “positive”/”normal” and “negative”/ “abnormal” examples The overall model structure is the same as an outlier-detection approach. The most important benefits of the new algorithm based on our algorithm are verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the USA, Western European as well as Russia. The test results are presented in the final part of the article.