AI 中文总结
针对熟练维护工人减少的问题，提出通过比较两个视频帧相似性检测异常帧来提取专家独特动作的方法，在模拟实验中对特定动作类型提取率达66.9%，比传统技术提高50个百分点，有助于技能转移和劳动力发展。
AI 中文摘要
关键基础设施（如铁路和发电厂）的维护对运营安全和可靠性至关重要。然而，熟练维护工人数量的减少对维持这些运营构成严峻挑战，凸显了有效将专家知识传授给经验不足工人的必要性。传统基于访谈的方法难以捕捉专家自身可能未意识到的知识。为此，我们提出一种方法，通过比较基于手册的工作视频和专家维护工人的视频来检测包含知识的候选动作的异常帧。在涉及配电板的模拟维护实验中，我们的方法针对手册中未描述的11种动作类型，提取率达到66.9%，比传统技术提高了50个百分点。这些发现强调了我们方法在揭示隐藏维护知识方面的有效性，有助于关键基础设施维护中的技能转移和劳动力发展。
英文摘要
Maintenance of critical infrastructures, such as railways and power plants, is essential for operational safety and reliability. However, the declining number of skilled maintenance workers poses a serious challenge to sustaining these operations, highlighting the need to effectively transfer expert know-how to less experienced workers. Although traditional interview-based approaches have been used to elicit maintenance skills, they struggle to capture know-how that experts themselves may not consciously recognize. To address this gap, we proposed a method that detects anomalous frames of candidate actions including know-how by comparing a video of manual-based work with that of expert maintenance workers. In a simulated maintenance experiment involving a distribution board, our method targeted 11 types of actions not described in the manual and achieved a 66.9% extraction rate, marking a 50-percentage-point improvement over conventional techniques. These findings underscore the effectiveness of our approach in revealing hidden maintenance knowledge, thereby contributing to enhanced skill transfer and workforce development in critical infrastructure maintenance.
Comments11 pages, 6 figures, 2 tables
