A Pilot Fatigue Prediction Method Based on Dynamic Bayesian Networks

IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Yao Zhou, Dengkai Chen, Jianghao Xiao, Yao Xiao, Yihui Lu, Youyi Zhang
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引用次数: 0

Abstract

Pilots of long-haul aircraft face a variety of challenges, including unstable flight environments, confined and narrow cockpit spaces, complex human–machine system operations, multiple tasks, and long-haul flight times. This study analyzed the factors leading to pilot fatigue from four aspects (human, machine, environment, task) and predicted the fatigue risk of long-haul flights using a dynamic Bayesian networks method. First, we identified factors related to fatigue during long-haul flights from four aspects: human, machine, environment, and task, and established an index system containing 20 fatigue risk factors. Second, 10 experts in the field of aviation evaluated these factors within the fatigue risk system to derive the prior probabilities for the dynamic Bayesian networks on pilot fatigue on long-haul flights. Finally, we introduced the Noisy-OR model to derive the conditional probabilities and calculated the posterior probabilities using the dynamic Bayesian networks. We validated the proposed method with a real case study, and the results showed that this method can predict fatigue during long-haul flights.

基于动态贝叶斯网络的飞行员疲劳预测方法
长途飞机的飞行员面临着各种各样的挑战,包括不稳定的飞行环境、狭窄的驾驶舱空间、复杂的人机系统操作、多任务、长途飞行时间等。本研究从人、机、环境、任务四个方面分析导致飞行员疲劳的因素,运用动态贝叶斯网络方法对长途飞行的疲劳风险进行预测。首先,从人、机、环境、任务四个方面识别长途飞行疲劳的相关因素,建立了包含20个疲劳危险因素的指标体系。其次,10名航空领域的专家对疲劳风险系统中的这些因素进行了评估,得出了远程飞行中飞行员疲劳的动态贝叶斯网络的先验概率。最后,我们引入了noise - or模型来推导条件概率,并利用动态贝叶斯网络计算后验概率。通过实际案例验证了该方法的有效性,结果表明该方法能够对长途飞行中的疲劳进行预测。
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来源期刊
CiteScore
5.20
自引率
8.30%
发文量
37
审稿时长
6.0 months
期刊介绍: The purpose of Human Factors and Ergonomics in Manufacturing & Service Industries is to facilitate discovery, integration, and application of scientific knowledge about human aspects of manufacturing, and to provide a forum for worldwide dissemination of such knowledge for its application and benefit to manufacturing industries. The journal covers a broad spectrum of ergonomics and human factors issues with a focus on the design, operation and management of contemporary manufacturing systems, both in the shop floor and office environments, in the quest for manufacturing agility, i.e. enhancement and integration of human skills with hardware performance for improved market competitiveness, management of change, product and process quality, and human-system reliability. The inter- and cross-disciplinary nature of the journal allows for a wide scope of issues relevant to manufacturing system design and engineering, human resource management, social, organizational, safety, and health issues. Examples of specific subject areas of interest include: implementation of advanced manufacturing technology, human aspects of computer-aided design and engineering, work design, compensation and appraisal, selection training and education, labor-management relations, agile manufacturing and virtual companies, human factors in total quality management, prevention of work-related musculoskeletal disorders, ergonomics of workplace, equipment and tool design, ergonomics programs, guides and standards for industry, automation safety and robot systems, human skills development and knowledge enhancing technologies, reliability, and safety and worker health issues.
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