Anomaly detection in flight recorder data: A dynamic data-driven approach

Proceedings of the American Control Conference

Volumn , Issue , 2013, Pages 2668-2673

  Das, Santanu ^a   Sarkar, Soumalya ^b   Ray, Asok ^b   Srivastava, Ashok ^a   Simon, Donald L ^c  

^a NASA AMES RESEARCH CENTER   (United States)

^b The Pennsylvania State University   (United States)

^c NASA GLENN RESEARCH CENTER   (United States)

Author keywords

Anomaly detection; Data driven analysis; Flight recorder data; Symbolic Dynamics

Indexed keywords

COMPUTATIONAL EFFICIENCY; DATA HANDLING; EXTRACTION; FEATURE EXTRACTION; RECORDING INSTRUMENTS; TIME SERIES; TIME SERIES ANALYSIS;

DATA-DRIVEN ANALYSIS; DETECTION ALGORITHM; FEATURE EXTRACTION ALGORITHMS; FLIGHT RECORDERS; LEVEL OF ABSTRACTION; PROBABILISTIC FINITE STATE AUTOMATON; SYMBOLIC DYNAMIC FILTERING; SYMBOLIC DYNAMICS;

ANOMALY DETECTION;

EID: 84883533871     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/acc.2013.6580237     Document Type: Conference Paper

References (14)

1. A. Ray, "Symbolic dynamic analysis of complex systems for anomaly detection, " Signal Processing, vol. 84, pp. 1115-1130, July 2004.
2. G. Mallapragada, A. Ray, and X. Jin, "Symbolic dynamic filtering and language measure for behavior identification of mobile robots, " IEEE Transactions on Sys-tems, Man, and Cybernetics Part B: Cybernetics, vol. 42, pp. 647-659, June 2012.
3. C. Bishop, Pattern Recognition and Machine learning. Springer, 2006.
4. M. Markou and S. Singh, "Novelty detection: A review-part 1: Statistical approaches, " Signal Processing, vol. 83, p. 2003, 2003.
5. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey, " ACM Comput. Surv., vol. 41, pp. 1-58, 2009.
6. I. C. Statler, "The Aviation System Monitoring and Modeling (ASMM) Project: A Documentation of its History and Accomplishments: 1999-2005, " 2007. Technical Report: NASA/TP-2007-214556.
7. Amidan, B. G., Swickard, A. R., Allen, R. E. and Ferryman T. A., "Identifying In-Close-Approach-Changes in Air Traffic Control (ATC) Data., " 2002. PNWD-3210. Battelle Pacific Northwest National Laboratory.
8. S. Budalakoti, A. N. Srivastava, and M. E. Otey, "Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety, " IEEE Transactions on Systems, Man, and Cy-bernetics, Part C: Applications and Reviews, vol. 39, no. 1, pp. 101-113, 2008.
9. S. Das, B. L. Matthews, A. N. Srivastava, and N. C. Oza, "Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study, " in Proc. 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, (New York, NY, USA), pp. 47-56, ACM, 2010.
10. L. Li, M. Gariel, R. J. Hansman, and R. Palacios, "Anomaly detection in onboard-recorded flight data using cluster analysis, " in Digital Avionics Systems Con-ference (DASC), 2011 IEEE/AIAA 30th, pp. 4A4-1-4A4-11, oct. 2011.
11. K. Bhaduri, B. L. Matthews, and C. R. Giannella, "Algorithms for speeding up distance-based outlier detection, " in Proc. 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, (New York, NY, USA), pp. 859-867, ACM, 2011.
12. S. D. Bay and M. Schwabacher, "Mining Distance-based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule, " in Proceedings of KDD'03, pp. 29-38, 2003.
13. V. Rajagopalan and A. Ray, "Symbolic time series analysis via wavelet-based partitioning, " Signal Processing, vol. 86, no. 11, pp. 3309-3320, 2006.
14. A. Subbu and A. Ray, "Space partitioning via hilbert transform for symbolic time series analysis, " Applied Physics Letters, vol. 92, pp. 084107-1, February 2008.