Cabour G, Ledoux É, Bassetto S
Department of Industrial Engineering, École Polytechnique de Montréal, Montréal, Canada.
Department of Physical Activity, Université du Québec à Montréal, Montréal, Canada.
Appl Ergon. 2022 Jul;102:103703. doi: 10.1016/j.apergo.2022.103703. Epub 2022 Mar 6.
Human inspectors rely on a significant number of macrocognitive functions, processes, and tacit knowledge to diagnose the condition of aircraft engine components. A deep understanding of inspectors' cognition and actions in the wild may establish the requirements to develop intelligent automation that truly enhances their perceptual, cognitive and social abilities. This paper takes a two-pronged approach to uncover and model the complexity of industrial inspection in a manner that aligns with the technical development stages of a Cyber-Physical-Social System. The findings offer thick descriptions accompanied by four descriptive empirical models that depict inspectors' meaning-making and decision-making processes. It includes how they gather, process, and apply domain-specific knowledge to diagnose a component's condition and how they deal with domain-related factors (norms, institution's rules, standard operating procedures). This study also highlights the support provided by empirical data/models in designers' work packages. It concludes by presenting the design implications of these findings to envision future human-automation work situations.
人工检查员依靠大量宏观认知功能、过程和隐性知识来诊断飞机发动机部件的状况。深入了解检查员在实际工作中的认知和行动，可能会为开发真正增强其感知、认知和社交能力的智能自动化系统确立要求。本文采用双管齐下的方法，以一种与网络物理社会系统的技术发展阶段相一致的方式，揭示并建模工业检查的复杂性。研究结果提供了详尽的描述，并伴有四个描述性实证模型，这些模型描绘了检查员的意义构建和决策过程。这包括他们如何收集、处理和应用特定领域的知识来诊断部件状况，以及他们如何处理与领域相关的因素（规范、机构规则、标准操作程序）。本研究还强调了实证数据/模型在设计师工作包中的支持作用。最后，本文阐述了这些研究结果对设想未来人机自动化工作场景的设计启示。
