Aligning work analysis and modeling with the engineering goals of a Cyber-Physical-Social System for industrial inspection

Abstract

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.