Intelligent Experience Retention System (IERS)

智能体验保留系统（IERS）

• 批准号: 542541-2019
• 负责人: Gaber, Hossam
• 金额: $ 1.35万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699409
• 关键词: Intelligent; Experience; Retention; System; IERS

In nuclear power plants (NPP), and at OPG, due to continuous moves of experienced personnel to different department or retirements, a vast amount of expertise and systems-specific knowledge is lost. This leads to longer training periods for new employees and sometimes to delayed responses to problems. The loss of expertise costs OPG a large amount of money as they have to invest in training less experienced staff, and leads to indirect losses in delayed or wrong activities, in particular in reactor maintenance and inspection activities. Moreover, the unavailability of expert systems limited the ability of staff to perform their tasks effectively. Another challenge and need to retain engineering knowledge is specific activities such as refurbishment or decommissioning, which are repeated over a long period of time (20-50 years) which did not enable building expertise in these areas, and transferring expertise became important. The challenges include: retirement of expertise in NPP; transformation and diversity of technologies in plant operation and maintenance; advancement in communications between human-human, machine-human, brain-machine, and machine-machine; and expensive and time consuming training of young generation plant engineers and personal. This project is aiming at the design of novel next generation operation experience support system by learning from engineering and operational data, as well as human experience and build comprehensive knowledgebase to support critical operation activities in timely manner. The project will demonstrate the development of Intelligent Experience Retention System (IERS) to be demonstrated with a number of case studies within OPG, prior to wide deployment in a number of sections. The project will include conceptual design, implementation, testing and AFS (Available For Service) of the IERS as a self-learning automated intelligent system that retains the personnel expertise, analyses it, and makes it ready to answer related questions asked by inexperienced employees.

在核电站（NPP）和OPG中，由于经验丰富的人员向不同的部门或退休的持续移动，丢失了大量的专业知识和特定于系统的知识。这导致新员工的培训期更长，有时会延迟对问题的回应。失去专业知识的损失使OPG花费了大量资金，因为他们必须投资于培训较少的员工，并导致延迟或错误活动的间接损失，尤其是在反应堆维护和检查活动中。此外，专家系统的不可用限制了员工有效执行任务的能力。另一个挑战和需要保留工程知识的是特定的活动，例如翻新或退役，这些活动在很长一段时间（20 - 50年）中重复了，这些活动无法在这些领域中建立专业知识，并且转移专业知识变得很重要。挑战包括：NPP的专业知识退休；植物运营和维护中技术的转变和多样性；人类，机器人，脑机和机器机器之间的通信发展；昂贵的且耗时的年轻植物工程师和个人培训。该项目的目的是通过从工程和运营数据中学习新颖的下一代操作体验支持系统的设计，以及人类的经验，并建立全面的知识基础，以及时支持关键的运营活动。该项目将证明智能体验保留系统（IERS）的发展将在OPG中的许多案例研究中证明，并在许多部分进行了广泛部署之前。该项目将包括概念设计，实施，测试和AFS（可用于服务）作为一个自学习的自动化智能系统，该系统保留了人员专业知识，对其进行分析，并准备好回答没有经验的员工问的相关问题。