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ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation

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ARFA : automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. / Kolev, Denis; Angelov, Plamen; Markarian, Garik; Suvorov, Michail; Lysanov, S.

Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on. Piscataway, N.J. : IEEE Press, 2013. p. 91-97.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Kolev, D, Angelov, P, Markarian, G, Suvorov, M & Lysanov, S 2013, ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. in Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on. IEEE Press, Piscataway, N.J., pp. 91-97. https://doi.org/10.1109/EAIS.2013.6604110

APA

Kolev, D., Angelov, P., Markarian, G., Suvorov, M., & Lysanov, S. (2013). ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. In Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on (pp. 91-97). IEEE Press. https://doi.org/10.1109/EAIS.2013.6604110

Vancouver

Kolev D, Angelov P, Markarian G, Suvorov M, Lysanov S. ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. In Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on. Piscataway, N.J.: IEEE Press. 2013. p. 91-97 https://doi.org/10.1109/EAIS.2013.6604110

Author

Kolev, Denis ; Angelov, Plamen ; Markarian, Garik ; Suvorov, Michail ; Lysanov, S. / ARFA : automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on. Piscataway, N.J. : IEEE Press, 2013. pp. 91-97

Bibtex

@inproceedings{4fde61376ac545538c4cf8b3b46491e9,
title = "ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation",
abstract = "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.",
author = "Denis Kolev and Plamen Angelov and Garik Markarian and Michail Suvorov and S. Lysanov",
year = "2013",
doi = "10.1109/EAIS.2013.6604110",
language = "English",
isbn = "9781467358552",
pages = "91--97",
booktitle = "Evolving and Adaptive Intelligent Systems (EAIS), 2013 IEEE Conference on",
publisher = "IEEE Press",

}

RIS

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 -