Cognitive-Neurodynamics-Accepted.pdf (1.26 MB)
A novel approach for pilot error detection using Dynamic Bayesian Networks
journal contribution
posted on 2015-07-02, 10:45 authored by Mohamad SaadaMohamad Saada, Qinggang MengQinggang Meng, Tingwen HuangIn the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system. © Springer Science+Business Media 2014.
History
School
- Science
Department
- Computer Science
Published in
Cognitive NeurodynamicsVolume
8Issue
3Pages
227 - 238Citation
SAADA, M., MENG, Q. and HUANG, T., 2014. A novel approach for pilot error detection using Dynamic Bayesian Networks. Cognitive Neurodynamics, 8 (3), pp. 227 - 238Publisher
© Springer Science+Business MediaVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2014Notes
This article was accepted for publication in the journal, Cognitive Neurodynamics [© Springer Science+Business Media]. The final publication is available at Springer via http://dx.doi.org/10.1007/s11571-013-9278-5ISSN
1871-4080eISSN
1871-4099Publisher version
Language
- en