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Unmanned Aircraft Systems: The Mitigation of Human Factors Errors Through Training

机译:无人机系统:通过培训减轻人为因素的错误

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摘要

Eighty percent of aviation accidents have been attributed to human factors errors. Human-centric design and human-in-the-loop studies attempt to allocate human tasks and computer automated tasks to reduce human error. There is a gap in the human error analyses pertaining to the highly automated design of unmanned aircraft systems forecast to be used in civil commercial flight operations. The problem addressed by this research is a lack of understanding of the causal or contributory human factors errors potentially thought to contribute to commercial unmanned aircraft accidents in the future. The purpose of this qualitative, nonexperimental, descriptive case study was to explore the aviation-specific relationship of human factors to technology in the complex human-machine operational environment. A Human Factors Analysis and Classification System-directed study was completed of 4 separate unmanned aircraft systems accidents. The study methods employed a coding schema to compile a list of complicit human factors errors. The list was subjected to a risk analysis and a Pareto prioritization to determine the top 20% of repetitive human errors. These data were verified through application of a Cohen's Kappa and a peer debriefing. By attaining understanding of the issues through this research project, macro training mitigation strategies were proposed to reduce the most prevalent human errors. One outcome of this project will be the inclusion of the findings in Federal Aviation Administration promulgated regulations designed to ensure positive social change by ensuring the safe integration of unmanned aircraft systems into the joint-use national airspace system.
机译:百分之八十的航空事故是由于人为因素造成的。以人为中心的设计和循环中的研究试图分配人为任务和计算机自动化任务以减少人为错误。人为失误分析与预测用于民用商业飞行的无人机系统的高度自动化设计存在差距。这项研究解决的问题是缺乏对因果关系或人为因素错误的理解,这些错误可能被认为是将来导致商业无人飞机事故的原因。此定性,非实验性描述性案例研究的目的是在复杂的人机操作环境中探索人为因素与技术之间航空特定的关系。一项针对人为因素分析和分类系统的研究完成了4例独立的无人机系统事故。研究方法采用编码方案来编制一系列复杂的人为因素错误。该列表经过风险分析和Pareto优先级确定,以确定重复性人为错误的前20%。这些数据通过应用Cohen的Kappa和同伴汇报进行了验证。通过本研究项目对问题的理解,提出了减少宏观培训的策略,以减少最普遍的人为错误。该项目的成果之一是将调查结果纳入联邦航空管理局颁布的法规中,旨在通过确保将无人驾驶飞机系统安全集成到联合使用的国家空域系统中来确保积极的社会变革。

著录项

  • 作者

    Neff, Peter S.;

  • 作者单位

    Walden University.;

  • 授予单位 Walden University.;
  • 学科 Public administration.;Transportation.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 243 p.
  • 总页数 243
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;
  • 关键词

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