Abstract Operating procedures are strictly followed in nuclear power plant operation. However, under a highly stressful condition such as emergency operation, human error probability can increase, with operators making mistakes in complying with the complex operating procedures. This paper proposes a procedure compliance check (PCC) system to monitor operator action and detect procedural deviation. If an operator action does not match the related procedural instruction, the PCC system notifies the operator in order to help them to recognize the mistake. A procedural logic process is constructed by referring to colored Petri nets. In situations requiring complex decisions, the PCC system employs a deep learning algorithm to predict operator judgement. The system was tested with data from a compact nuclear simulator, and demonstrated its potential to detect procedural noncompliance.

中文翻译：

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基于深度学习的核电厂应急运行程序合规性检查系统

摘要 核电厂运行严格遵守操作规程。然而，在紧急操作等高度紧张的情况下，人为错误的可能性会增加，操作员在遵守复杂的操作程序时会出错。本文提出了一种程序合规性检查 (PCC) 系统来监控操作员的行为并检测程序偏差。如果操作员的动作与相关的程序指令不匹配，PCC 系统会通知操作员以帮助他们识别错误。一个过程逻辑过程是通过参考有色 Petri 网来构建的。在需要复杂决策的情况下，PCC 系统采用深度学习算法来预测操作员的判断。该系统使用来自紧凑型核模拟器的数据进行测试，