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

Santanu Das, Soumalya Sarkar, Asok Ray, Ashok Srivastava, Donald L. Simon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

This paper presents a method of feature extraction in the context of aviation data analysis. The underlying algorithm utilizes a feature extraction algorithm called symbolic dynamic filtering (SDF) that was recently published. In SDF, time-series data are partitioned for generating symbol sequences that, in turn, construct probabilistic finite state automata (PFSA) to serve as features for pattern classification. The SDF-based algorithm of feature extraction, which enjoys both flexibility of implementation and computational efficiency, is directly applicable to detection, classification, and prediction of anomalies and faults. The results of analysis with real-world flight recorder data show that the SDF-based features can be derived at a desired level of abstraction from the information embedded in the time-series data. The performance of the proposed SDF-based feature extraction is compared with that of standard temporal feature extraction for anomaly detection. Our study on flight recorder data shows that SDF-based features can enable discovering unique anomalous flights and improve the performance of the detection algorithm. We also theoretically show that under certain conditions it may be possible to achive a better or comparable time complexity with SDF based features.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages2668-2673
Number of pages6
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Das, S., Sarkar, S., Ray, A., Srivastava, A., & Simon, D. L. (2013). Anomaly detection in flight recorder data: A dynamic data-driven approach. In 2013 American Control Conference, ACC 2013 (pp. 2668-2673). [6580237] (Proceedings of the American Control Conference).