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: $ 1.41万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720669
• 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，由于经验丰富的人员不断流动到不同的部门或退休，大量的专业知识和系统特定的知识丢失。这导致新员工的培训时间更长，有时对问题的反应也会延迟。专业知识的损失使OPG花费了大量资金，因为他们不得不投资于培训经验不足的工作人员，并导致延迟或错误活动的间接损失，特别是在反应堆维护和检查活动中。此外，专家系统的缺乏限制了工作人员有效执行任务的能力。保留工程知识的另一个挑战和需要是具体活动，如翻新或退役，这些活动在很长一段时间内（20-50年）重复进行，无法在这些领域建立专门知识，转让专门知识变得重要。这些挑战包括：核电站专业知识的退休;电厂运行和维护技术的转变和多样性;人与人、机与人、脑与机和机与机之间通信的进步;以及对年轻一代电厂工程师和个人的昂贵而耗时的培训。该项目旨在通过学习工程和运营数据以及人类经验，设计新一代运营经验支持系统，并建立全面的知识库，以及时支持关键运营活动。该项目将展示智能体验保留系统（IERS）的开发，在OPG内进行大量案例研究，然后在多个部分进行广泛部署。该项目将包括IERS的概念设计、实施、测试和AFS（可供服务），作为一个自学自动化智能系统，保留人员的专业知识，进行分析，并准备回答经验不足的员工提出的相关问题。