A novel approach for pilot error detection using Dynamic Bayesian Networks

Abstract

In 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.

Attention is shaped by semantic level of event-structure during speech comprehension: an electroencephalogram study

“…Recently, the predictive coding theory has become a highly influential theory of perceptual processing. It proposes that attention operates to optimize the precision of perceptual inference, causing prediction errors (or, equivalently, sensory data) to be weighted (Knill and Pouget 2004;Friston 2005Friston , 2009Feldman and Friston 2010;Hesselmann et al 2009;Hohwy 2012;Rao 2005;Saada et al 2014). Both studies related to general sensory processing and those related to speech processing have demonstrated that prediction reduces sensory activities (e. g., Alink et al 2010;den Ouden et al 2010;Todorovic et al 2011;Sohoglu et al 2012), and thus have provided supporting evidence for the predictive coding theory.…”

Pilot Error? Managerial Decision Biases as Explanation for Disruptions in Aircraft Development

“…On the psychological side, human factors play a significant role in complex technical environments in which important judgments and decisions must be made (e.g., Kahneman & Tversky, ; Meyer, ). To take an aerospace example, according to recent estimates, more than one half of flight accidents are attributable to “pilot error” (Saada, Meng, & Huang, ). Could the management of major aircraft development programs be liable for similar “pilot errors” in their judgment and decision making?…”

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

“…• In [26], an algorithm is proposed for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data, based 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.…”