Original scientific paper
https://doi.org/10.7307/ptt.v38i2.1028
Pilot Workload Identification During Engine Failure Landings
Jiajun YUAN
; Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
Huaxian LIN
; Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
Chenyang ZHANG
orcid.org/0000-0003-3409-5493
; School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, China
Xijun KE
; Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
Panpan YU
; Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
Qingfeng ZHANG
; Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
*
* Corresponding author.
Abstract
Pilot workload is a critical factor affecting flight safety, particularly in emergency situations such as engine failure during forced landings. This study aims to assess pilot workload during engine failure-induced forced landings compared to normal landings using EEG signals. EEG data were recorded from 21 pilots using a flight simulator and EEG acquisition equipment during both forced and normal landings. Subjective workload scores were obtained using a subjective workload scale. EEG features were extracted, and nonparametric tests were applied to assess the significance of these features at different workload levels. Subsequently, machine learning algorithms (SVC, KNN, RF and LightGBM) were employed to develop models for workload evaluation. The LightGBM model achieved a peak accuracy of 99.5% using the top 80% of all features (time-domain, frequency-domain and nonlinear features) as inputs. This study provides an effective quantitative approach for assessing pilot workload during emergency situations and offers valuable insights for improving flight safety and optimising pilot training programs.
Keywords
aviation safety; engine failure; workload; machine-learning; EEG
Hrčak ID:
344906
URI
Publication date:
24.2.2026.
Visits: 263 *