ABSTRACT

Objective

The current effort develops an initial integrated model of fatigue in the context of C-17 approach and landing operations. Specifically, we integrate a biomathematical fatigue model with a task network model to estimate pilot performance degradation.

Background

Fatigue risk management is a critical process in aviation and flight deck operations, given its dramatic impact on aviation safety and pilot health. Biomathematical fatigue models are useful tools in several aviation fatigue risk management programs that can be embedded in electronic device applications. However, these tools are limited in terms of identifying specific performance outcomes affected by fatigue, as well as individualizing fatigue estimates to individual pilots. Integrating computational cognitive models and biomathematical fatigue models can help address these issues.

Methods

Forty-four aircrew members completed the study with 10 performing 33 landings with sets of corresponding actigraph data and C-17 performance metrics. We developed a task network model of C-17 approach and landing operations and integrated biomathematical fatigue model predictions based on actigraph data from the aircrew. We then compared predictions from this integrated model with C-17 performance metrics from the missions.

Results

We successfully predicted delays in setting flaps, landing gear, and engaging the speed brake.

Conclusion

After further development and validation, this integrated model can be implemented within an application to provide real-time information on pilot fatigue and expected performance on specific aircraft operations.

中文翻译：

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疲劳对 C-17 进近和着陆性能影响的综合建模

摘要

客观的

目前的工作是在 C-17 进近和着陆操作的背景下开发疲劳的初始综合模型。具体来说，我们将生物数学疲劳模型与任务网络模型相结合，以估计飞行员的性能退化。

背景

鉴于疲劳风险管理对航空安全和飞行员健康的巨大影响，它是航空和驾驶舱操作中的一个关键过程。生物数学疲劳模型是多种航空疲劳风险管理程序中的有用工具，可以嵌入电子设备应用程序中。然而，这些工具在识别受疲劳影响的具体绩效结果以及对个别飞行员进行个性化疲劳估计方面存在局限性。整合计算认知模型和生物数学疲劳模型可以帮助解决这些问题。

方法

44 名机组人员完成了这项研究，其中 10 人执行了 33 次着陆，并提供了相应的活动记录数据集和 C-17 性能指标。我们开发了 C-17 进场和着陆操作的任务网络模型，并根据机组人员的活动记录数据开发了综合生物数学疲劳模型预测。然后，我们将这个集成模型的预测与来自任务的 C-17 性能指标进行了比较。

结果

我们成功地预测了设置襟翼、起落架和接合减速板的延迟。

结论

经过进一步开发和验证后，可以在应用程序中实施此集成模型，以提供有关飞行员疲劳和特定飞机操作的预期性能的实时信息。