Abstract
The Quick Access Recorder (QAR) is an airborne equipment designed to store raw flight data, which contains a mass amount of safety related parameters such as flap angle, airspeed, altitude, etc. The assessment of QAR data is of great significance for the safety of civil aviation and the improvement of pilots skills. The existing QAR assessment approaches mainly utilizes the exceedance detection (ED) that relies on the pre-defined parameter threshold, which could miss potential flight risks. In this paper, we perform anomaly detection on the takeoff and landing phases based on an improved random forest (RF) method. The evaluation is performed on the dataset generated by a fleet of B-737NG, which shows that the method is able to discriminate the high-severity events accurately on the high dimensional multivariate time series, which also shows that the model can identify the events with potential risk pattern on the imbalanced dataset even if the event has not been pre-defined before.
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References
Guidance on the establishment of a Flight Data Analysis Program (FDAP) - Safety Management Systems (SMS)
Qi, M.: Astronautics: flight operations risk diagnosis method on quick-access-record exceedance. J. Beijing Univ. Aeronaut. Astronaut. 37, 1207–1210 (2011)
Shao, X., Qi, M., Gao, M.: A risk analysis model of flight operations based on region partition. Kybernetes 41, 1497–1508 (2012)
Sun, R.S., Han, W.L.: Technology: analysis on parameters characteristics of flight exceedance events based on distinction test. J. Saf. Sci. Technol. (2011)
Wang, L., Wu, C., Sun, R.: Pilot operating characteristics analysis of long landing based on flight QAR data. In: Harris, D. (ed.) EPCE 2013. LNCS, vol. 8020, pp. 157–166. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39354-9_18
Li, L., Gariel, M., Hansman, R.J., Palacios, R.: Anomaly detection in onboard-recorded flight data using cluster analysis. In: Digital Avionics Systems Conference (2011)
Zheng, L., Hong, C., Shao, X.Y.: Pattern Recognition and Risk Analysis for Flight Operations (2017)
Janic, M.: An assessment of risk and safety in civil aviation. J. Air Transp. Manag. 6, 43–50 (2000)
Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom. Intell. Lab. Syst. 83, 83–90 (2006)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417 (1933)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)
He, H., Garcia, E.A.: Learning from imbalanced data. Trans. Knowl. Data Eng. 21, 1263–1284 (2009)
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Li, J., Zhang, H., Yang, J. (2019). Discrimination Model of QAR High-Severity Events Using Machine Learning. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_36
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DOI: https://doi.org/10.1007/978-3-030-36204-1_36
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