The Analysis of the Contribution of Human Factors to the In-flight Loss of Control Accidents

Ancel, Ersin; Shih, Ann T.

doi:10.2514/6.2012-5548

Cited by 17 publications

(7 citation statements)

References 2 publications

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“…Analyzing several accident reports of the last 10 years has shown that the main causes of these accidents [11][12][13] were human failure and their respective physiological aspects (human factors) [14]. It is therefore possible to note that the aviation safety is facing an age of accident factors caused by human failure.…”

Section: Motivationmentioning

confidence: 99%

β-Band Analysis from Simulated Flight Experiments

Roza

Postolache

2021

Aerospace

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Several safety-related improvements are applied every year to try to minimize the total number of civil aviation accidents. Fortunately, these improvements work well, reducing the number of accident occurrences. However, while the number of accidents due to mechanical failures has decreased, the number of accidents due to human errors seems to grow. On that basis, this work presents a contribution regarding the brain’s β-band activities for different levels of volunteers’ expertise on flight simulator, i.e., experienced, mid-level and beginner, in which they acted as pilots in command during several simulated flights. Spectrogram analysis and statistical measurements of each volunteer’s brain’s β-band were carried out. These were based on seven flight tasks: takeoff, climb, cruise flight, descent, approach, final approach and landing. The results of the proposed experiment showed that the takeoff, approach and landing corresponded to the highest brain activities, i.e., close to 37.06–67.33% more than the brain activity of the other flight tasks: when some accidents were about to occur, the intensities of the brain activity were similar to those of the final approach task. When the volunteers’ expertise and confidence on flight simulation were considered, it was shown that the highest brain magnitudes and oscillations observed of more experienced and confident volunteers were on average close to 68.44% less, compared to less experienced and less confident volunteers. Moreover, more experienced and confident volunteers in general presented different patterns of brain activities compared to volunteers with less expertise or less familiarity with fight simulations and/or electronic games.

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Section: Motivationmentioning

confidence: 99%

β-Band Analysis from Simulated Flight Experiments

Roza

Postolache

2021

Aerospace

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“…Another advantage of using the Bayesian approach is that the models are capable of taking into consideration both objective and subjective measures of uncertainty. 17,18 Compared to their manned counterparts, input data for UAS operation safety assessments contain significant sources of uncertainty. These uncertainties include: a) the lack of UAS operational data and experience, b) use of non-regulated COTS components, and c) a vast range of UAS configurations (multi-rotor, fixed wing, and hybrid setups), which present challenges to identification, collection, and assessment of aircraft failure modes and their impacts.…”

Section: Bayesian Approachmentioning

confidence: 99%

“…The Bayesian approach has also been extensively used to represent and evaluate uncertainty in aviation safety applications in addition to a large number of other complex reliable systems, such as the nuclear power industry, cyber security, space launch industry, etc. [14][15][16][17][18][19] Assessing the operational risk of flying unmanned aircraft over populated areas requires a systems perspective approach that considers the system to be a series of interconnected and interacting parts, together delivering some desirable outcome and unintended consequences. 16…”

Section: Bayesian Approachmentioning

confidence: 99%

Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

Ancel

Capristan

Foster

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over nonparticipant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Traffic Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off-nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.

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“…The aspect of multi-dependency is modelled well through the BBN technique using software called Hugin Explorer 12 . The technique is built upon nodes that represent the causal factors and arrows to each node that represent the causal dependency between the two or more factors 13 . A high-level generalized BBN is used to model the LOS incidents and accidents, respectively, as depicted in Figs.…”

Section: Risk-based Causal Modelingmentioning

confidence: 99%

Risk-based Causal Modeling of Airborne Loss of Separation

Geuther

Shih

2015

15th AIAA Aviation Technology, Integration, and Operations Conference

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Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.

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The Analysis of the Contribution of Human Factors to the In-flight Loss of Control Accidents

Cited by 17 publications

References 2 publications

β-Band Analysis from Simulated Flight Experiments

β-Band Analysis from Simulated Flight Experiments

Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

Risk-based Causal Modeling of Airborne Loss of Separation

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