The Relationship Between Flight Operations and Organizations in Aircraft Accidents; The Application of the Human Factor Analysis and Classification System

Dönmez, Kadir; Uslu, Suat

doi:10.18038/aubtda.348219

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…During instructional flights, especially solo flights, pilots become busier with many important tasks (e.g., ATC communication and controlling flight path in final approach and landing phases. In line with previous studies, we found that nearly 72 percent of all accidents (n:70) occurred in final approach and landing phase of the flight [39]. Figure 6).…”

Section: Discussionsupporting

confidence: 92%

“…Human Factor Analysis and Classification System (HFACS) HFACS is one of the most widely used comprehensive and reliable analytical frameworks to examine and classify causal factors of accidents and incidents in various industries such as medicine [31], construction [32], maritime [33], mining industry [34], oil and gas [35], railways [36] and aviation [11,[37][38][39] FAA also uses it for the investigation of causality of the accidents and incidents [40].…”

Section: 4mentioning

confidence: 99%

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HFACS Analysis for Investigating Human Errors in Flight Training Accidents

Kılıç

2019

Journal of Aviation

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Training of pilots in civil aviation is the primary concern when dealing with accidents and incidents. There has been too little research into accidents during flight instruction. To advance the literature, the present study aims to analyze accidents during the instructional general aviation flights. This study examined investigation reports of 70 accidents involving instructional general aviation flights in the United States between 01.01.2018 and 12.12.2018. The accident data obtained from the National transportation safety board (NTSB) accident data system. The causal factors of those accidents were statistically analyzed and classified by using the Human Factors Analysis and Classification System (HFACS). To the best of our knowledge, this paper is the first ever report analyzing contributing factors of training flight accidents by the analytical framework HFACS. According to the analysis results, it was revealed that across all accidents mentioned above, skill-based errors were ranked as the primary contributing factors, followed by environmental factors, and decision errors. Results showed that the most common skill-based errors are student pilots' improper landing flare and failure to maintain directional control during landing and takeoff. The findings of this study provide intriguing insight that may be beneficial to people researching contributing factors of aviation accidents in academia and practice. Furthermore, the outcome of the current study may assist student pilots in completing safe flights.

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Section: Discussionsupporting

confidence: 92%

Section: 4mentioning

confidence: 99%

HFACS Analysis for Investigating Human Errors in Flight Training Accidents

Kılıç

2019

Journal of Aviation

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“…In the first category, there are three subcategories: adverse mental state, adverse physiological state and physical/mental limitations (Shappell & Wiegmann, 2000). Dönmez and Uslu (2018) concluded that 35% of the preconditions for unsafe acts are related to the adverse mental state of the operators, to which the cognitive fatigue of airline pilots contributes significantly.…”

Section: Cognitive Fatigue and Accidentsmentioning

confidence: 99%

The impact of cognitive fatigue on airline pilots performance

Quental

Rocha

Silva

et al. 2021

J. airl. airpt. manag.

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Purpose: Air transport is a highly regulated branch of aviation, but it continues to show occurrences where human error is present. Fatigue is now recognized as a hazard which degrades human performance and can put flight safety at risk. In this regard, the general objective of this study is to assess the impact of cognitive fatigue on airline pilots and how it can contribute to the occurrence of accidents and incidents.Methodology: Three airline pilots participated in the case study. The participants’ cognitive fatigue was monitored according to four methods, being two of them of a subjective nature – the sleep diary (SD) and the Samn-Perelli 7-Point Fatigue Scale (SPS), and the other two of an objective nature – the Psychomotor Vigilance Task (PVT) and the actigraphy (actiwatch ReadibandTM 5). During their flight duty periods (FDPs), the pilots’ performance was also assessed according to the score delivered by a fatigue management software (FAID®).Findings: The obtained results allowed to understand whether the pilots are aware of their alertness and to identify factors which affect their performance levels. Between the beginning and the end of each FDP, significant changes were observed concerning the assessment on the SPS scale, the reaction time (RT) and the fatigue score generated by the biomathematical models associated to the technique of the actigraphy and the software FAID®.Originality/value: The risk of accident or serious error was classified according to the four methodologies used. Thus, it is possible to verify if there is a correspondence between the different scales or if there are scales more conservative (with a higher associated risk) than others.

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“…Increased operational capabilities of aircraft had considerably modified the missions of pilots and introduce new problematics. For example, long periods of intense and sustained cognitive activities induce cognitive fatigue that is known to impair the performance of reasoned cognitive processing tasks over a period and also to be one of the major risks of incidents/accidents in aviation [e.g., Holtzer et al (2010) , Marcus and Rosekind (2017) , Dehais et al (2018) , and Dönmez and Uslu (2018) ]. In this study, we aimed at furthering our understanding of the influence of cognitive fatigue on alarm detection in order to develop passive brain–computer interfaces (pBCIs) based on explainable artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Classification of Electrophysiological Signatures With Explainable Artificial Intelligence: The Case of Alarm Detection in Flight Simulator

Massé

Bartheye

Fabre

2022

Front. Neuroinform.

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Relevant sounds such as alarms are sometimes involuntarily ignored, a phenomenon called inattentional deafness. This phenomenon occurs under specific conditions including high workload (i.e., multitasking) and/or cognitive fatigue. In the context of aviation, such an error can have drastic consequences on flight safety. This study uses an oddball paradigm in which participants had to detect rare sounds in an ecological context of simulated flight. Cognitive fatigue and cognitive load were manipulated to trigger inattentional deafness, and brain activity was recorded via electroencephalography (EEG). Our results showed that alarm omission and alarm detection can be classified based on time-frequency analysis of brain activity. We reached a maximum accuracy of 76.4% when the algorithm was trained on all participants and a maximum of 90.5%, on one participant, when the algorithm was trained individually. This method can benefit from explainable artificial intelligence to develop efficient and understandable passive brain–computer interfaces, improve flight safety by detecting such attentional failures in real time, and give appropriate feedback to pilots, according to our ambitious goal, providing them with reliable and rich human/machine interactions.

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The Relationship Between Flight Operations and Organizations in Aircraft Accidents; The Application of the Human Factor Analysis and Classification System

Cited by 3 publications

References 22 publications

HFACS Analysis for Investigating Human Errors in Flight Training Accidents

HFACS Analysis for Investigating Human Errors in Flight Training Accidents

The impact of cognitive fatigue on airline pilots performance

Classification of Electrophysiological Signatures With Explainable Artificial Intelligence: The Case of Alarm Detection in Flight Simulator

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