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 - ARFA
T2 - automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation
AU - Kolev, Denis
AU - Angelov, Plamen
AU - Markarian, Garik
AU - Suvorov, Michail
AU - Lysanov, S.
PY - 2013
Y1 - 2013
N2 - In this paper a novel approach to autonomous real time flight data analysis (FDA) is proposed and investigated. The anomaly detection is based on recursive density estimation (RDE) and the fault identification is based on the evolving self-learning classifiers introduced recently. The paper starts with a brief critical analysis of the currently used FDA methods and tools. Then the problems of fault detection (FD) and identification are described formally. The importance of the ability to process the data in real time and on-line (in flight) is directly related to the efficiency and safety. Therefore, in this paper the focus is on the recursive approaches which are computationally lean and suitable for on-line mode of operation. The novel concept of ARFA (Automated Real-time FDA) is then applied to real flight data from Russian and USA made aircrafts. The results are compared and analyzed. Both, advantages that this novel methodology and algorithms offer as well as the current limitations and future directions of research are pointed out and future work outlined.
AB - In this paper a novel approach to autonomous real time flight data analysis (FDA) is proposed and investigated. The anomaly detection is based on recursive density estimation (RDE) and the fault identification is based on the evolving self-learning classifiers introduced recently. The paper starts with a brief critical analysis of the currently used FDA methods and tools. Then the problems of fault detection (FD) and identification are described formally. The importance of the ability to process the data in real time and on-line (in flight) is directly related to the efficiency and safety. Therefore, in this paper the focus is on the recursive approaches which are computationally lean and suitable for on-line mode of operation. The novel concept of ARFA (Automated Real-time FDA) is then applied to real flight data from Russian and USA made aircrafts. The results are compared and analyzed. Both, advantages that this novel methodology and algorithms offer as well as the current limitations and future directions of research are pointed out and future work outlined.
U2 - 10.1109/EAIS.2013.6604110
DO - 10.1109/EAIS.2013.6604110
M3 - Conference contribution/Paper
SN - 9781467358552
SP - 91
EP - 97
BT - Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on
PB - IEEE Press
CY - Piscataway, N.J.
ER -