Failing And Not Falling (F&!F): Data-Enabled Classification Learning of Aircraft Accidents and Incidents

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jarrod Carson ◽  
Kane Hollingsworth ◽  
Rituparna Datta ◽  
Aviv Segev
Keyword(s):  
Classification Learning ◽  
Aircraft Accidents

An Alternative Method for Identifying a Failed Engine in Twin-Engine Propeller Aircraft

2020 ◽  
Vol 10 (2) ◽  
pp. 103-111
Author(s):  
Andrey K. Babin ◽  
Andrew R. Dattel ◽  
Margaret F. Klemm
Keyword(s):  
Human Error ◽  
Flight Simulator ◽  
Traditional Group ◽  
Aircraft Accidents ◽  
Alternative Method ◽  
Indicator Group ◽  
Group A ◽  
The Dead ◽  
Engine Failure ◽  
And Performance

Abstract. Twin-engine propeller aircraft accidents occur due to mechanical reasons as well as human error, such as misidentifying a failed engine. This paper proposes a visual indicator as an alternative method to the dead leg–dead engine procedure to identify a failed engine. In total, 50 pilots without a multi-engine rating were randomly assigned to a traditional (dead leg–dead engine) or an alternative (visual indicator) group. Participants performed three takeoffs in a flight simulator with a simulated engine failure after rotation. Participants in the alternative group identified the failed engine faster than the traditional group. A visual indicator may improve pilot accuracy and performance during engine-out emergencies and is recommended as a possible alternative for twin-engine propeller aircraft.

Action information from classification learning

2006 ◽  
Author(s):  
Brian H. Ross ◽  
Ranxiao F. Wang ◽  
Arthur F. Kramer ◽  
Daniel J. Simons ◽  
James A. Crowell
Keyword(s):  
Classification Learning

scholarly journals Cloud icing by mineral dust and impacts to aviation safety

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Slobodan Nickovic ◽  
Bojan Cvetkovic ◽  
Slavko Petković ◽  
Vassilis Amiridis ◽  
Goran Pejanović ◽  
...  
Keyword(s):  
Mineral Dust ◽  
Weather Conditions ◽  
Ice Nucleation ◽  
Aircraft Accidents ◽  
Ice Nuclei ◽  
Loss And Damage ◽  
Cloud Ice ◽  
Convective Weather ◽  
First Time ◽  
Engine Power

AbstractIce particles in high-altitude cold clouds can obstruct aircraft functioning. Over the last 20 years, there have been more than 150 recorded cases with engine power-loss and damage caused by tiny cloud ice crystals, which are difficult to detect with aircraft radars. Herein, we examine two aircraft accidents for which icing linked to convective weather conditions has been officially reported as the most likely reason for catastrophic consequences. We analyze whether desert mineral dust, known to be very efficient ice nuclei and present along both aircraft routes, could further augment the icing process. Using numerical simulations performed by a coupled atmosphere-dust model with an included parameterization for ice nucleation triggered by dust aerosols, we show that the predicted ice particle number sharply increases at approximate locations and times of accidents where desert dust was brought by convective circulation to the upper troposphere. We propose a new icing parameter which, unlike existing icing indices, for the first time includes in its calculation the predicted dust concentration. This study opens up the opportunity to use integrated atmospheric-dust forecasts as warnings for ice formation enhanced by mineral dust presence.

scholarly journals Frequency-Following Response (FFR) in Military Pilots

2020 ◽  
Author(s):  
Graziela Maria Martins-Moreira ◽  
Alessandra Spada Durante
Keyword(s):  
Pure Tone Audiometry ◽  
Control Group ◽  
Study Group ◽  
Aircraft Accidents ◽  
Frequency Following Response ◽  
Significant Difference ◽  
Qualitative Variables ◽  
Chi Squared ◽  
The Right ◽  
Military Pilots

Abstract Introduction Good hearing in pilots, including central auditory skills, is critical for flight safety and the prevention of aircraft accidents. Pure tone audiometry alone may not be enough to assess hearing in the members of this population who, in addition to high noise levels, routinely face speech recognition tasks in non-ideal conditions. Objective To characterize the frequency-following response (FFR) of a group of military pilots compared with a control group. Methods Twenty military pilots in the Study Group and 20 non-pilot military personnel, not exposed to noise in their work, in the Control Group, all with normal hearing, aged between 30 and 40 years old, completed a questionnaire to assess their hearing habits, and their FFRs were measured with a /da/ syllable (duration 40 milliseconds, speed 10.9/s), at 80 dB NA in the right ear. All procedures were approved by the ethical committee of the institution. Statistical analysis was performed using the t-Student or Mann-Whitney tests for quantitative variables, and the Fisher or chi-squared tests for qualitative variables, and a value of p < 0.05 was considered to be statistically significant. Results There was no significant difference between the groups regarding auditory habits. In the FFR, wave amplitudes A (p = 0.01) and C (p = 0.04) were significantly lower in the Study Group. Conclusion Working as a military pilot can be a crucial factor in determining an individual's typical FFR pattern, demonstrated in the present study by statistically significant reductions in the amplitudes of the A and C waves.

A Bayesian-Network-based Approach to Risk Analysis in Runway Excursions

2019 ◽  
Vol 72 (5) ◽  
pp. 1121-1139 ◽  
Author(s):  
Fernando Calle-Alonso ◽  
Carlos J. Pérez ◽  
Eduardo S. Ayra
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Exploratory Analysis ◽  
Hypothesis Tests ◽  
Aircraft Accidents ◽  
Risk Scenarios ◽  
Key Variables ◽  
Expert Recommendations

Aircraft accidents are extremely rare in the aviation sector. However, their consequences can be very dramatic. One of the most important problems is runway excursions, when an aircraft exceeds the end (overrun) or the side (veer-off) of the runway. After performing exploratory analysis and hypothesis tests, a Bayesian-network-based approach was considered to provide information from risk scenarios involving landing procedures. The method was applied to a real database containing key variables related to landing operations on three runways. The objective was to analyse the effects over runway overrun excursions of failing to fulfil expert recommendations upon landing. For this purpose, the most influential variables were analysed statistically, and several scenarios were built, leading to a runway ranking based on the risk assessed.

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