Data-driven modeling for aviation safety diagnosis and prognosis

Xiaoge Zhang, Yingxiao Kong, Abhinav Subramanian, Sankaran Mahadevan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The safety of the air transportation system is affected by a variety of uncertainties arising from multiple sources. This paper investigates a diagnosis and prognosis approach to detect anomalies in the flight trajectory, diagnose root causes, and then perform prognosis regarding the risk of occurrence of adverse events, in the presence of various sources of uncertainty. The proposed method is illustrated using a three-step procedure. First, using flight trajectory data, we evaluate the probabilities of system states corresponding to each failure case, from which we formulate a state-space model. Next, we perform anomaly detection for a specific flight trajectory by developing a Bayesian state estimation-based method, and subsequently identify the cause of the detected anomaly. Once the root cause is identified, prognosis is performed to predict the future state in a probabilistic manner. The proposed method is illustrated using near-ground landing data synthetically generated from an open source air traffic simulator - BlueSky. The simulation data mimicking the near-ground landing process with different initial states (e.g., aircraft altitude and speed, response delay, and brake performance) and other factors (such as wind direction) are used to demonstrate the procedures of diagnosis and prognosis.

Original languageEnglish
Title of host publicationPHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society
EditorsAnibal Bregon, Marcos Orchard
PublisherPrognostics and Health Management Society
Number of pages8
Volume10
ISBN (Electronic)9781936263295
DOIs
Publication statusPublished - 24 Sep 2018
Externally publishedYes
Event10th Annual Conference of the Prognostics and Health Management Society, PHM 2018 - Philadelphia, United States
Duration: 24 Sep 201827 Sep 2018

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
ISSN (Print)2325-0178

Conference

Conference10th Annual Conference of the Prognostics and Health Management Society, PHM 2018
Country/TerritoryUnited States
CityPhiladelphia
Period24/09/1827/09/18

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

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