评估技术人员在纠正维护诊断方面的工作量和绩效.
Hyunjong Shin1, Ling Rothrock1, Vittaldas Prabhu1
1Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
智能诊断支持系统可以提高维护生产率. 与人工智能诊断支持系统 (AId-system) 相比,故障树诊断支持系统 (FTd-system) 提高了15%的任务完成时间,而不会增加技术人员的工作量.
科学领域:
- 工程 工程师 工程师 工程师
- 人与计算机的交互
- 工业系统 工业系统
背景情况:
- 数字技术正在改变行业,使智能产品服务系统能够提高生产力.
- 维护任务通常涉及身体和认知工作,为智能系统集成提供了机会.
- 人与智能系统的交互至关重要,因为维护的完全自动化并不总是可行的.
研究的目的:
- 调查智能技术,特别是不同的诊断支持系统,对技术人员在维护期间的工作量和绩效的影响.
- 将断层树诊断支持系统 (FTd-system) 与人工智能诊断支持系统 (AId-system) 的有效性进行比较.
主要方法:
- 使用复制计算机数控 (CNC) 机器关键组件的模型进行了受控实验室实验.
- 45名参与者被分为三个组:FTd系统,AId系统和没有支持的对照组.
- 测量了技术人员完成任务的时间和工作量.
主要成果:
- FTd-系统组完成的维护任务比AId-系统组快15%.
- 在这三组人群中,工作量没有显著差异.
- 进一步分析表明,系统接口设计显著影响任务完成时间.
结论:
- 智能诊断支持系统可以通过减少诊断尝试而提高维护生产率,而不会增加技术人员的工作量.
- 优化用户界面,例如显示更少的选项,可以进一步减少维护时间和认知负载.
- 这些发现表明,通过智能支持系统,可以在手动维护中显著提高效率.
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