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Evaluating Impact of AI on Cognitive Load of Technicians During Diagnosis Tasks in Maintenance

Abstract : Even today, many maintenance activities are still done manually because maintenance is one of the most difficult areas to be automated in manufacturing. Many technicians spend their time on non-technical activities such as retrieving instructions from manuals. If AI (Artificial Intelligence) can alleviate some of these tasks, the time to diagnosis and repair can be shortened. However there are limited works about the effects of using AI during maintenance activities on a technician’s cognitive load. Therefore, as an initiative, we conducted a pilot experiment with 10 participants to analyze the effects of the AI-based support system on diagnosis tasks in the manufacturing. In the experiment, participants were divided into two groups: the group used an AI-based support system and the other group used a Fault Tree (FT) based support system; two groups’ mean task completion time and task load of participants using NASA Task Load were measured. According to the experiment results, the group which used the AI-based support system to diagnose the model completed task 53% lesser time than the group which used the FT-based support system. In addition, participants who used the AI-based support system reported relatively lower task loads compared to participants who used the FT-based support system. This experiment results imply that maintenance time and a variability can be reduced if an AI-based support system supports maintenance technicians.
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Hyunjong Shin, Vittaldas Prabhu. Evaluating Impact of AI on Cognitive Load of Technicians During Diagnosis Tasks in Maintenance. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.27-34, ⟨10.1007/978-3-319-99707-0_4⟩. ⟨hal-02177865⟩

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