Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator

Full text
Author(s):	Kraemer, Aline D. ; Villani, Emilia ; Arjoni, Diego H. Total Authors: 3
Document type:	Journal article
Source:	IFAC PAPERSONLINE; v. 51, n. 34, p. 6-pg., 2019-01-01.
Abstract
This paper presents a human factors analysis in aviation within the context of failure detection and identification (FDI) using statistical data analysis and clustering. We used data from experiments in a motion-based flight simulator (SIVOR) with 4 experienced pilots performing a take-off maneuvre under three conditions: normal, under engine failure and under flap failure. We propose two metrics based on statistical data analysis to evaluate and compare human behavior during flight. We also use k-means clustering in order to classify flights according to maneuvre conditions and misclassified flights are further analyzed according to which pilot has performed it. Results show that for the statistical data analysis the behavior of one specific pilot has higher dissimilarity with all other pilots. Moreover, for the k-means clustering, most of the misclassified flights were performed by this same pilot. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. (AU)

FAPESP's process:	12/51085-3 - Flight simulator assisted by a robotic motion platform
Grantee:	Luís Gonzaga Trabasso
Support Opportunities:	Research Grants - Research Partnership for Technological Innovation - PITE