Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Incremental anomaly identification by adapted SVM method
AU - Suvorov, Michail
AU - Ivliev, Sergey
AU - Markarian, Garik
AU - Kolev, Denis
AU - Zvikhachevskiy, Dmitry
AU - Angelov, Plamen
PY - 2013/8
Y1 - 2013/8
N2 - 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.
AB - 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.
U2 - 10.1109/IJCNN.2013.6707031
DO - 10.1109/IJCNN.2013.6707031
M3 - Conference contribution/Paper
SN - 9781467361286
SP - 1
EP - 8
BT - International Joint Conference on Neural Networks, IJCNN-2013, Dallas, TX, USA, 3-9 August, 2013
PB - IEEE
CY - Piscataway, N.J.
ER -